Industrial chemistry and manufacturing technologies Books

Book SynopsisThis book is an update and expansion of topics covered in Guidelines for Mechanical Integrity Systems (2006). The new book is consistent with Risk-Based Process Safety and Life Cycle approaches and includes details on failure modes and mechanisms. Also, example testing an inspection programs is included for various types of equipment and systems. Guidance and examples are provided for selecting and maintaining critical safety systems.Table of ContentsList of Figures xi List of Tables xiii Preface xvii Acknowledgments xix Files on the Web xxi 1 Introduction 1 1.1 Background and Scope 1 1.2 What is Asset Integrity Management? 2 1.3 What Assets are Included? 5 1.4 AIM Life Cycle 7 1.5 Relationship to Other Programs 7 1.6 Relationship to RAGAGEP 8 1.7 Structure of this Document 12 Chapter 1 References 15 2 Management Responsibility 17 2.1 Leadership Roles and Responsibilities 17 2.2 Technical Assurance Responsibilities 25 Chapter 2 References 29 3 AIM Life Cycle 31 3.1 Overview 31 3.2 Research Through Process Development 33 3.3 Process Design 35 3.4 Engineering, Procurement and Construction 39 3.5 Commissioning 40 3.6 Operation and Maintenance 42 3.7 Decommissioning 44 3.8 RAGAGEP Selection and Application at Each Stage 45 Chapter 3 References 47 Appendix 3A. Design Review Suggestions 49 4 Failure Modes and Mechanisms 53 4.1 Introduction 53 4.2 Equipment Functions and Functional Failure 54 4.3 Failure Modes 57 4.4 Damage Mechanisms 61 4.5 Failure Effects 64 4.6 Risk 65 4.7 Analysis 66 4.8 ITPM Task Assignments 69 4.9 Operational Issues 69 4.10 Other Related Activities 70 Chapter 4 References 70 Appendix 4A. Risk Concepts Related to AIM 73 5 Asset Selection and Criticality Determination 77 5.1 Program Objectives and Philosophy 77 5.2 Asset Selection Criteria and Principles 79 5.3 Level of Detail 84 5.4 Asset Criticality Determination 86 5.5 Documentation 96 5.6 Roles and Responsibilities 97 Chapter 5 References 99 Appendix 5A. Sample Guidelines for Selecting Assets for an AIM Program 100 6 Inspection, Testing and Preventive Maintenance 105 6.1 ITPM Task Planning 107 6.2 ITPM Task Execution and Monitoring 128 6.3 ITPM Program Roles and Responsibilities 137 Chapter 6 References 141 Appendix 6A. Common Predictive Maintenance and Nondestructive Testing (NDT) Techniques for Mechanical Equipment 142 7 Established Approaches for Developing Test and Inspection Plans 171 7.1 Code/Standard Approaches 171 7.2 Regulatory Authority Approaches 172 7.3 Company-Specific Approaches 172 7.4 Risk-Based Inspection (RBI) 173 7.5 Failure Modes, Effects and Criticality Analysis Approaches 176 7.6 Safety Instrumented Systems 179 Chapter 7 References 184 8 AIM Training and Performance Assurance 187 8.1 Skills and Knowledge Assessment 189 8.2 Training For New and Current Workers 191 8.3 Verification and Documentation of Performance Assurance 193 8.4 Certifications 194 8.5 Ongoing and Refresher Training 195 8.6 Training for Maintenance Technicians and Operators Performing Maintenance Tasks 197 8.7 Training for Technical Personnel 200 Contents ix 8.8 Contractor Issues 202 8.9 Roles and Responsibilities 203 Chapter 8 References 206 Appendix 8A. Sample Training Survey 207 Appendix 8B. Sample Training Guide 208 9 Asset Integrity Procedures 211 9.1 Types of Procedures Supporting the AIM Program 213 9.2 Identification of Needs 216 9.3 Procedure Development Process 220 9.4 Format and Content 223 9.5 Other Sources of AIM Procedures 226 9.6 Implementing and Maintaining AIM Procedures 227 9.7 AIM Procedure Program Roles and Responsibilities 229 Chapter 9 References 229 Appendix 9A. Example AIM Procedure 232 10 Quality Management 239 10.1 Design 241 10.2 Procurement 243 10.3 Fabrication 244 10.4 Receiving 246 10.5 Storage and Retrieval 247 10.6 Construction and Installation 248 10.7 In-service Repairs, Alterations and Rerating 250 10.8 Temporary Installations and Temporary Repairs 252 10.9 Decommissioning / Re-use 254 10.10 Used Assets 255 10.11 Spare Parts 256 10.12 Contractor-Supplied Assets and Materials 256 10.13 QA Program Roles and Responsibilities 257 Chapter 10 References 257 Appendix 10A. Sample Vendor QA Plan 260 Appendix 10B. Positive Material Identification 262 Appendix 10C. Sample Service Contractor QA Plan 266 11 Equipment Deficiency Management 269 11.1 Equipment Deficiency Management Process 270 11.2 Acceptance Criteria 270 11.3 Equipment Deficiency Identification 274 11.4 Responding to Equipment Deficiencies 275 11.5 Equipment Deficiency Communication 278 11.6 Tracking of Temporary Repairs 279 11.7 Deficiency Management Roles and Responsibilities 279 Chapter 11 References 280 12 Equipment-Specific Integrity Management 287 12.1 Vessels, Tanks and Piping 289 12.2 Relief and Vent Systems 294 12.3 Instrumentation and Controls 297 12.4 Rotating Equipment 300 12.5 Fired Equipment 303 12.6 Electrical Systems 304 12.7 Fire Protection and Suppression Systems 305 12.8 Ventilation and Purge Systems 306 12.9 Protective Systems 307 12.10 Passive Mitigation Systems 309 12.11 Solids-Handling Systems 310 12.12 Refrigeration Systems 311 12.13 Utilities 311 12.14 Safety Equipment 311 Chapter 12 References 314 Appendix 12A. Asset Integrity Activities by Equipment Type 317 13 AIM Program Implementation 383 13.1 Budgeting and Resources 383 13.2 Use of Data Management Systems 396 13.3 AIM Benefits and Return on Investment 400 Chapter 13 References 402 Appendix 13A. AIM Program Design Activity Worksheets 403 14 Metrics, Audits and Continuous Improvement: Learning from Experience 409 14.1 Performance Measurement and Monitoring 411 14.2 AIM Program and Implementation Audits 420 14.3 Continuous Improvement 427 Chapter 14 References 429 Appendix 14A. AIM-Related Regulatory Citations 430 15 Other Asset Management Tools 437 15.1 Introduction to Common Risk-based Analytical Techniques Used in AIM Programs 437 15.2 Incorporating Risk into AIM Decisions 443 15.3 Reliability-Centered Maintenance 445 15.4 Protection Layer Analysis Techniques 448 15.5 Asset Failure and Root Cause Analyses 451 Chapter 15 References 457 Acronyms and Abbreviations 459 Glossary 463 Index 469

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Book SynopsisThis book describes how to conduct Process Hazard Assessments (PHAs) for processes handling combustible solids. The book explains how to do a dust hazard assessment by using either an approach based on compliance with existing consensus standards, or by using a risk based approach.Table of ContentsList of Tables xiii List of Figures xv Acronyms and Abbreviations xvii Glossary xix Acknowledgments xxiv Preface xxvii 1. Introduction 1 1.1 Purpose of Book 1 1.2 Book Road Map 2 1.3 References 4 2. Background 5 2.1 Nature of the Dust Fire and Explosion Problem 5 2.1.1 Dust Explosion Statistics 5 2.1.2 Case Study: Hoeganaes Corporation 5 Findings and Lessons 10 2.2 Requirements for Dust Fires and Explosions 11 2.2.1 Layer Fire. 12 2.2.2 Flash Fires and Explosions 12 2.3 Combustibility and Explosivity Parameters 15 2.3.1 Explosibility Screening Test 15 2.3.2 Deflagration Index, KSt (bar-m/sec) 18 2.3.3 Maximum Pressure, Pmax (Bar) 18 2.3.4 Minimum Explosible Concentration, MEC (g/m3) 19 2.3.5 Minimum Ignition Energy, MIE (mJoules, mJ) 19 2.3.6 Minimum Auto Ignition Temperature, Cloud, MAIT (°C) 20 2.3.7 Layer Ignition Temperature, LIT (°C) 20 2.3.8 Limiting Oxygen Concentration, LOC (vol% O2) 20 2.3.9 Volume Resistivity (Ohm-m) 20 2.4 Comparison to Combustible Vapors 21 2.5 Effect of Parameters 22 2.6 Summary 22 2.7 References 23 3. The Hazards Within – Dust Inside Equipment 25 3.1 Methods of Prevention, Protection, Mitigation 25 3.1.1 Ignition Control 26 3.1.2 Inerting/Oxidant Control 28 3.1.3 Combustible Concentration Control 28 3.1.4 Deflagration Venting 28 3.1.5 Deflagration Suppression 29 3.1.6 Containment 29 3.1.7 Deflagration Isolation 30 3.2 Issues 30 3.2.1 Air/Material Separators 32 3.2.2 Size Reduction Equipment (grinders, mills, etc.) 34 3.2.3 Dryers 35 3.2.4 Silos/Hoppers 36 3.2.5 Portable Containers 37 3.2.6 Conveyors 38 3.2.7 Blenders/Mixers 41 3.2.8 Feeding into Vessels Having Flammable Vapor Atmospheres 41 3.3 Summary 42 3.4 References 42 4. Hazards of Dust External to Equipment 45 4.1 Case Study – Imperial Sugar 45 4.2 Issues Inside a Room or Building 48 4.3 Methods of Prevention and Protection 49 4.3.1 Control of Dust Deposits Outside of Equipment 49 4.3.2 Ignition Control 52 4.3.3 Damage Limiting Construction 52 4.4 Summary 52 4.5 References 53 5. Traditional Approach to Hazard Assessment and Control 55 5.1 Introduction 55 5.1.1 Process Safety Information (PSI) 55 5.1.2 Competent Team 56 5.2 Steps to the Traditional Approach 56 5.2.1 Step 1 – Is a combustible dust involved? 57 5.2.2 Step 2 – Determine Which Standards Apply 58 5.2.3 Step 3 - Determine Where Fire/Explosion Hazards Exist 62 5.2.4 Step 4 – Review Unit Operation vs. Standard Requirements for Prevention and Mitigation of Fires/Explosions 63 5.2.5 Step 5 – Make Recommendations 65 5.2.6 Step 6 – Document the DHA 65 5.2.7 Step 7 – Implement the Recommendations 66 5.3 Summary 67 5.4 References 68 6. Risk-based Approach to Dust Hazard Analysis 69 6.1 Introduction 69 6.2 Technique for a Risk-based DHA 70 6.2.1 Step 1: Identify Failure Scenarios 70 6.2.2 Step 2: Evaluate the Consequences 70 6.2.3 Step 3: Are the Consequences Tolerable? 73 6.2.4 Step 4: Estimate Likelihood and Risk 73 6.2.5 Step 5: Is the Risk Tolerable 78 6.2.6 Step 6: Recommend and Evaluate Solutions 80 6.2.7 Step 7: Is the Mitigated Risk Tolerable? 81 6.2.8 Step 8: Document Results 81 6.3 DHA Risk Assessment, Additional Requirements 82 6.3.1 DHA Leader Competency 83 6.3.2 Documentation 83 6.4 Managing Change and Updating Risk Assessment 83 6.5 Summary 83 6.6 References 84 7. Special Considerations: Combustible Dust Issues in Existing Facilities 87 7.1 Introduction 87 7.2 Existing Facilities and Combustible Dusts 87 7.2.1 Potential Issues 87 7.2.2 Issues Impact 91 7.2.3 Precautions 92 7.3 Summary 92 7.4 References 93 8. Worked Examples 95 8.1 Introduction 95 8.2 Example 1 95 8.2.1 Process Description – Example 1 95 8.2.2 Traditional DHA – Example 1 95 8.2.3 Risk-based DHA – Example 1 112 8.2.4 Comparison of Traditional vs. Risk-based Approach – Example 1 167 8.3 Example 2 169 8.3.1 Process Description 2 169 8.3.2 Traditional DHA 171 8.3.3 Risk-based DHA 173 8.3.4 Comparison of Traditional vs. Risk-based Approach – Example 2 176 8.4 Example 3 177 8.4.1 Process Description – Example 3 177 8.4.2 Traditional DHA – Example 3 179 8.4.3 Risk-based DHA – Example 3 181 8.5 Summary 188 8.6 References 188 Appendix A Regulations and Codes 191 A.1 Regulations 191 A.1.1 U.S. 191 A.1.2 International 191 A.2 Codes 192 References 195 Appendix B Additional Resources 197 B.1 Books 197 B.2 U.S. Chemical Safety Board Reports 197 B.3 Journal Articles 198 B.4 Other 199 Appendix C Data for Risk-based DHA 201 C.1 Probability Assessment of Process Unit Fire or Dust Explosion 201 C.1.1 Initiating Event Frequencies 204 C.1.2 Ignition Probabilities 205 C.1.3 Protection Layer PFDs 207 C.2 References 209 Appendix D Good Practices 211 D.1 Self Assessment 211 D.2 Housekeeping 213 D.2.1 Combustible Dust Housekeeping Inspection Checklist 215 D.3 Explosion Protection Methods 217 Appendix E DHA Roadmap 219 Notes for Figure E.1 221 Index 223

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Book SynopsisENGINEERING APPLICATIONS A comprehensive text on the fundamental principles of mechanical engineering Engineering Applications presents the fundamental principles and applications of the statics and mechanics of materials in complex mechanical systems design. Using MATLAB to help solve problems with numerical and analytical calculations, authors and noted experts on the topic Mihai Dupac and Dan B. Marghitu offer an understanding of the static behaviour of engineering structures and components while considering the mechanics of materials knowledge as the most important part of their design. The authors explore the concepts, derivations, and interpretations of general principles and discuss the creation of mathematical models and the formulation of mathematical equations. This practical text also highlights the solutions of problems solved analytically and numerically using MATLAB. The figures generated with MATLAB reinforce visual learning for students andTable of Contents1 Forces 1 1.1 Terminology and Notation 1 1.2 Resolution of Forces 3 1.3 Angle Between Two Forces 3 1.4 Force Vector 4 1.5 Scalar (Dot) Product of Two Forces 5 1.6 Cross Product of Two Forces 5 1.7 Examples 6 2 Moments and Couples 15 2.1 Types of Moments 15 2.2 Moment of a Force About a Point 15 2.3 Moment of a Force About a Line 18 2.4 Couples 20 2.5 Examples 21 3 Equilibrium of Structures 55 3.1 Equilibrium Equations 55 3.2 Supports 57 3.3 Free-Body Diagrams 59 3.4 Two-Force and Three-Force Members 60 3.5 Plane Trusses 61 3.6 Analysis of Simple Trusses 62 3.6.1 Method of Joints 62 3.6.2 Method of Sections 65 3.7 Examples 67 4 Centroids and Moments of Inertia 129 4.1 Centre of the Mass and Centroid 129 4.2 Centroid and Centre of the Mass of a Solid Region, Surface or Curve 130 4.3 Method of Decomposition 134 4.4 First Moment of an Area 134 4.5 The Centre of Gravity 135 4.6 Examples 136 5 Stress, Strain and Deflection 185 5.1 Stress 185 5.2 Elastic Strain 185 5.3 Shear and Moment 186 5.4 Deflections of Beams 189 5.5 Examples 193 6 Friction 211 6.1 Coefficient of Static Friction 212 6.2 Coefficient of Kinetic Friction 213 6.3 Friction Models 213 6.3.1 Coulomb Friction Model 214 6.3.2 Coulomb Model with Viscous Friction 216 6.3.3 Coulomb Model with Stiction 217 6.4 Angle of Friction 218 6.5 Examples 219 7 Work, Energy and Power 255 7.1 Work 255 7.2 Kinetic Energy 256 7.3 Work and Power 258 7.4 Conservative Forces 259 7.5 Work Done by the Gravitational Force 259 7.6 Work Done by the Friction Force 260 7.7 Potential Energy and Conservation of Energy 261 7.8 Work Done and Potential Energy of an Elastic Force 261 7.9 Potential Energy Due to the Gravitational Force 262 7.9.1 Potential Energy Due to the Gravitational Force for a Particle 262 7.9.2 Potential Energy Due to the Gravitational Force for a Rigid Body 263 7.10 Examples 264 8 Simple Machines 295 8.1 Load and Effort, Mechanical Advantage, Velocity Ratio and Efficiency of a Simple Machine 295 8.1.1 Load and Effort 295 8.1.2 Mechanical Advantage 296 8.1.3 Velocity Ratio and Efficiency 296 8.2 Effort and Load of an Ideal Machine 297 8.3 The Lever 297 8.4 Inclined Plane (Wedge) 298 8.5 Screws 299 8.6 Simple Screwjack 299 8.6.1 Motion Impending Upwards 301 8.6.2 Motion Impending Downwards 302 8.6.3 Efficiency While Hoisting Load 303 8.7 Differential Screwjack 303 8.8 Pulleys 304 8.8.1 First-order Pulley System 304 8.8.2 Second-order Pulley System 306 8.8.3 Third-order Pulley System 307 8.9 Differential Pulley 308 8.10 Wheel and Axle 309 8.11 Wheel and Differential Axle 310 8.12 Examples 312 References 353 Index 357

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Book SynopsisThis book is a short introduction to the engineering principles of harnessing the vast potential of microorganisms, and animal and plant cells in making biochemical products. It was written for scientists who have no background in engineering, and for engineers with minimal background in biology. The overall subject dealt with is process.Table of ContentsPreface xvii About the CompanionWebsite xix 1 An Overview of Bioprocess Technology and Biochemical Engineering 1 1.1 A Brief History of Biotechnology and Biochemical Engineering 1 1.1.1 Classical Biotechnology 1 1.1.2 Recombinant DNA 4 1.1.3 A Typical Bioprocess 6 1.1.4 Biochemical Engineering and Bioprocess Technology 8 1.2 Industrial Organisms 10 1.2.1 Prokaryotes 12 1.2.1.1 Eubacteria and Archaea 12 1.2.2 Eukaryotic Microorganisms 12 1.2.2.1 Fungi 13 1.2.2.2 Algae 13 1.2.3 Multicellular Organisms andTheir Cells 13 1.2.3.1 Insect Cells 13 1.2.3.2 Plant Cells, Tissues, and Organs 13 1.2.3.3 Animal Cells, Tissues, and Organs 14 1.2.4 Transgenic Plants and Animals 14 1.3 Biotechnological Products 15 1.3.1 Metabolic Process 15 1.3.2 Metabolites 18 1.3.3 Cells, Tissues, and Their Components 19 1.3.3.1 Viruses 20 1.3.4 Secreted Enzymes and Other Biopolymers 20 1.3.5 Recombinant DNA Products 20 1.3.5.1 Heterologous rDNA Proteins 20 1.3.6 Metabolic Engineering and Synthetic Pathways 22 1.4 Technology Life Cycle, and Genomics- and Stem Cell-Based New Biotechnology 23 1.4.1 The Story of Penicillin and the Life Cycle of Technology 23 1.4.2 Genomics, Stem Cells, and Transformative Technologies 25 Further Reading 26 Problems 26 2 An Introduction to Industrial Microbiology and Cell Biotechnology 29 2.1 Universal Features of Cells 29 2.2 Cell Membranes, Barriers, and Transporters 30 2.3 Energy Sources for Cells 31 2.3.1 Classification of Microorganisms According toTheir Energy Source 32 2.4 Material and Informational Foundation of Living Systems 34 2.4.1 All Cells Use the Same Molecular Building Blocks 34 2.4.2 Genes 34 2.4.3 Genetic Information Processing 36 2.5 Cells of Industrial Importance 36 2.5.1 Prokaryotes 38 2.5.2 Eubacteria 38 2.5.2.1 CellWall and Cell Membrane 38 2.5.2.2 Membrane and Energy Transformation 40 2.5.2.3 Differentiation 41 2.5.3 Archaea 42 2.5.4 Eukaryotes 43 2.5.4.1 The Nucleus 44 2.5.4.2 Mitochondrion 45 2.5.4.3 Endoplasmic Reticulum and Golgi Apparatus 46 2.5.4.4 Other Organelles 47 2.5.4.5 Cytosol 48 2.6 Cells Derived from Multicellular Organisms 49 2.7 Concluding Remarks 50 Further Reading 50 Problems 50 3 Stoichiometry of Biochemical Reactions and Cell Growth 53 3.1 Stoichiometry of Biochemical Reactions 53 3.1.1 Metabolic Flux at Steady State 58 3.1.1.1 NAD/NADH Balance in Glycolysis 59 3.1.1.2 OxidativeMetabolism and NADH 60 3.1.2 Maximum Conversion of a Metabolic Product 63 3.2 Stoichiometry for Cell Growth 66 3.2.1 Cell Composition and Material Flow to Make Cell Mass 66 3.2.1.1 Composition and Chemical Formula of Cells 66 3.2.1.2 Material Flow for Biomass Formation 69 3.2.2 Stoichiometric Equation for Cell Growth 70 3.2.2.1 Yield Coefficient 71 3.3 Hypothetical Partition of a Substrate for Biomass and Product Formation 73 3.4 Metabolic Flux Analysis 74 3.4.1 Analysis of a Chemical Reaction System 74 3.4.1.1 Setting Up Material Balance Equations 74 3.4.1.2 Quasi–Steady State 76 3.4.1.3 Stoichiometric Matrix, Flux Vectors, and Solution 76 3.4.2 Analysis of Fluxes in a Bioreaction Network 77 3.4.3 Metabolic Flux Analysis on a Cellular System 81 3.4.3.1 Selecting Reactions for Analysis 81 3.4.3.2 Compartmentalization 83 3.4.3.3 Biomass 83 3.4.3.4 Limitations on Accounting of Materials 84 3.4.3.5 Solution and Analysis 84 3.5 Concluding Remarks 85 Further Reading 85 Nomenclature 86 Problems 86 4 Kinetics of Biochemical Reactions 95 4.1 Enzymes and Biochemical Reactions 95 4.2 Mechanics of Enzyme Reactions 96 4.3 Michaelis–Menten Kinetics 98 4.4 Determining the Value of Kinetic Parameters 101 4.5 Other Kinetic Expressions 104 4.6 Inhibition of Enzymatic Reactions 106 4.7 Biochemical Pathways 108 4.7.1 Kinetic Representation of a Reaction Pathway 108 4.7.2 Linearity of Fluxes in Biochemical Pathways 110 4.8 Reaction Network 114 4.9 Regulation of Reaction Rates 114 4.9.1 Flux Modulation by Km 114 4.9.2 Allosteric Regulation of Enzyme Activities 115 4.9.3 Regulation at Transcriptional and Posttranslational Levels 117 4.9.4 Modulation of Resource Distribution through Reversible Reactions 118 4.10 Transport across Membrane and Transporters 120 4.10.1 Transport across the Cell Membrane 120 4.10.2 Transport of Electrolytes 121 4.10.3 Transport of Charged Molecules across Membrane 122 4.10.4 Types of Transporters 123 4.10.5 Kinetics of a Facilitated Transporter 124 4.11 Kinetics of Binding Reactions 126 4.11.1 Binding Reactions in Biological Systems 126 4.11.2 Dissociation Constant 127 4.11.3 Saturation Kinetics 128 4.11.4 Operator Binding and Transcriptional Regulation 129 4.11.5 Kinetics of Transcription and Translation 131 4.12 Concluding Remarks 135 Further Reading 136 Nomenclature 136 Problems 138 5 Kinetics of Cell Growth Processes 145 5.1 Cell Growth and Growth Kinetics 145 5.2 Population Distribution 148 5.3 Description of Growth Rate 149 5.4 Growth Stage in a Culture 150 5.5 Quantitative Description of Growth Kinetics 151 5.5.1 Kinetic Description of Substrate Utilization 153 5.5.2 Using the Monod Model to Describe Growth in Culture 155 5.6 Optimal Growth 156 5.7 Product Formation 158 5.8 Anchorage-Dependent Vertebrate Cell Growth 159 5.9 Other Types of Growth Kinetics 161 5.10 Kinetic Characterization of Biochemical Processes 162 5.11 Applications of a Growth Model 163 5.12 The Physiological State of Cells 164 5.12.1 MultiscaleModel Linking Biotic and Abiotic Phases 166 5.13 Kinetics of Cell Death 168 5.14 Cell Death and the Sterilization of Medium 169 5.15 Concluding Remarks 171 Further Reading 172 Nomenclature 172 Problems 173 6 Kinetics of Continuous Culture 183 6.1 Introduction 183 6.2 Kinetic Description of a Continuous Culture 185 6.2.1 Balance Equations for Continuous Culture 185 6.2.2 Steady-State Behavior of a Continuous Culture 187 6.2.2.1 Monod Kinetics 187 6.2.2.2 Steady-State Concentration Profiles 187 6.2.2.3 Washout 189 6.2.3 Productivity in Continuous Culture 190 6.3 Continuous Culture with Cell Recycling 193 6.3.1 Increased Productivity with Cell Recycling 193 6.3.2 Applications of Continuous Culture with Cell Recycling 196 6.3.2.1 Low Substrate Levels in the Feed 196 6.3.2.2 Low Residual Substrate Concentration 197 6.3.2.3 Labile Product 197 6.3.2.4 Selective Enrichment of Cell Subpopulation 197 6.3.2.5 High-Intensity Mammalian Cell Culture 197 6.4 Specialty Continuous Cultures 199 6.4.1 Multiple-Stage Continuous Culture 199 6.4.2 Immobilized Cell Culture System 200 6.4.3 Continuous Culture with Mixed Populations 201 6.5 Transient Response of a Continuous Culture 202 6.5.1 Pulse Increase at the Substrate Level 203 6.5.2 Step Change in Feed Concentration 204 6.6 Concluding Remarks 205 Further Reading 205 Nomenclature 205 Problems 206 7 Bioreactor Kinetics 217 7.1 Bioreactors 217 7.2 Basic Types of Bioreactors 218 7.2.1 Flow Characteristics in Idealized Stirred-Tank (Well-Mixed) and Tubular (Plug Flow) Reactors 219 7.2.2 Reaction in an Idealized CSTR 220 7.2.3 Reaction in an Idealized PFR 222 7.2.4 Heterogeneous and Multiphasic Bioreactors – Segregation of Holding Time 225 7.3 Comparison of CSTR and PFR 225 7.3.1 CSTR versus PFR in Conversion Yield and Reaction Rate 225 7.3.2 CSTR versus PFR in Terms of Nutrient Depletion and Scale-Up 226 7.3.3 CSTR versus PFR – A Perspective from Residence Time Distribution 227 7.4 Operating Mode of Bioreactors 229 7.4.1 Batch Cultures 229 7.4.2 Fed-Batch Cultures 229 7.4.2.1 Intermittent Harvest 229 7.4.2.2 Fed-Batch 230 7.5 Configuration of Bioreactors 231 7.5.1 Simple Stirred-Tank Bioreactor 231 7.5.2 Airlift Bioreactor 233 7.5.3 Hollow-Fiber Bioreactor 233 7.6 Other Bioreactor Applications 233 7.7 Cellular Processes through the Prism of Bioreactor Analysis 235 7.8 Concluding Remarks 236 Further Reading 236 Nomenclature 237 Problems 237 8 Oxygen Transfer in Bioreactors 241 8.1 Introduction 241 8.2 Oxygen Supply to Biological Systems 242 8.3 Oxygen and Carbon Dioxide Concentration in Medium – Henry’s Law 243 8.4 Oxygen Transfer through the Gas–Liquid Interface 244 8.4.1 A Film Model for Transfer across the Interface 244 8.4.2 Concentration Driving Force for Interfacial Transfer 245 8.4.3 Mass Transfer Coefficient and Interfacial Area 246 8.5 Oxygen Transfer in Bioreactors 248 8.5.1 Material Balance on Oxygen in a Bioreactor 249 8.5.2 Oxygen Transfer in a Stirred Tank 251 8.6 ExperimentalMeasurement of KLa and OUR 253 8.6.1 Determination of KLa in a Stirred-Tank Bioreactor 253 8.6.2 Measurement of OUR and qO2 254 8.7 Oxygen Transfer in Cell Immobilization Reactors 256 8.8 Concluding Remarks 256 Further Reading 256 Nomenclature 256 Problems 258 9 Scale-Up of Bioreactors and Bioprocesses 265 9.1 Introduction 265 9.2 General Considerations in Scale Translation 266 9.2.1 Process and Equipment Parameters Affected by Scale-Up 266 9.2.2 Scale Translation for Product Development and Process Troubleshooting 266 9.2.3 How Scale-Up Affects Process Variables, Equipment, and Cellular Physiology 267 9.2.4 Scale-Up of Equipment and Geometrical Similarity 267 9.3 Mechanical Agitation 268 9.4 Power Consumption and Mixing Characteristics 269 9.4.1 Power Consumption of Agitated Bioreactors 269 9.4.2 Other Dimensionless Numbers 272 9.4.3 Correlation of Oxygen Transfer Coefficient 273 9.5 Effect of Scale on Physical Behavior of Bioreactors 273 9.6 Mixing Time 276 9.6.1 Nutrient Enrichment Zone: Mixing Time versus Starvation Time 276 9.6.2 Mixing Time 277 9.6.3 Mixing Time Distribution 278 9.7 Scaling Up and Oxygen Transfer 279 9.7.1 Material Balance on Oxygen in Bioreactor 279 9.7.1.1 Aeration Rate and the Oxygen Transfer Driving Force 280 9.8 Other Process Parameters and Cell Physiology 281 9.9 Concluding Remarks 282 Further Reading 283 Nomenclature 283 Problems 284 10 Cell Culture Bioprocesses and Biomanufacturing 289 10.1 Cells in Culture 289 10.2 Cell Culture Products 290 10.2.1 Vaccines 290 10.2.2 Therapeutic Proteins 291 10.2.3 Biosimilars 292 10.3 Cellular Properties Critical to Biologics Production 294 10.3.1 Protein Secretion 294 10.3.1.1 Folding in the Endoplasmic Reticulum 294 10.3.1.2 Membrane Vesicle Translocation and Golgi Apparatus 295 10.3.2 Glycosylation 296 10.3.3 Protein Secretion and Glycan Heterogeneity 296 10.4 Nutritional Requirements 299 10.4.1 Chemical Environment In Vivo and in Culture 299 10.4.2 Types of Media 300 10.4.2.1 Basal Medium and Supplements 300 10.4.2.2 Complex Medium, Defined Medium 301 10.5 Cell Line Development 301 10.5.1 Host Cells and Transfection 301 10.5.2 Amplification 302 10.6 Bioreactors 304 10.6.1 Roller Bottles 304 10.6.2 Stirred-Tank Bioreactors for Suspension Cells 305 10.6.3 Stirred-Tank Bioreactor with Microcarrier Cell Support 306 10.6.4 Disposable Systems 307 10.7 Cell Retention and Continuous Processes 307 10.7.1 Continuous Culture and Steady State 307 10.8 Cell Culture Manufacturing – Productivity and Product Quality 308 10.8.1 Process and Product Quality 308 10.8.2 Product Life Cycle 309 10.8.3 Product Manufacturing 311 10.8.3.1 Platform Process 311 10.8.3.2 Manufacturing 311 10.9 Concluding Remarks 312 Further Reading 312 Problems 313 11 Introduction to Stem Cell Bioprocesses 319 11.1 Introduction to Stem Cells 319 11.2 Types of Stem Cells 320 11.2.1 Adult Stem Cells 320 11.2.1.1 Hematopoietic Stem Cells 321 11.2.1.2 Mesenchymal Stem Cells 323 11.2.1.3 Neuronal Stem Cells 323 11.2.2 Embryonic Stem Cells 324 11.2.3 Induced Pluripotent Stem Cells and Reprogramming 324 11.3 Differentiation of Stem Cells 326 11.4 Kinetic Description of Stem Cell Differentiation 328 11.5 StemCell Technology 331 11.6 Engineering in Cultivation of Stem Cells 332 11.7 Concluding Remarks 335 Further Reading 335 Nomenclature 336 Problems 336 12 Synthetic Biotechnology: FromMetabolic Engineering to Synthetic Microbes 339 12.1 Introduction 339 12.2 Generalized Pathways for Biochemical Production 340 12.3 General Strategy for Engineering an Industrial, Biochemical-Producing Microorganism 342 12.3.1 Genomics, Metabolomics, Deducing Pathway, and Unveiling Regulation 342 12.3.2 Introducing Genetic Alterations 343 12.3.3 Isolating Superior Producers 345 12.3.3.1 Screening of Mutants with the Desired Phenotype 345 12.3.3.2 Selection of Mutants with the Target Trait 345 12.3.4 Mechanisms of Enhancing the Biosynthetic Machinery 347 12.3.4.1 Relaxing the Constriction Points in the Pathway 347 12.3.4.2 Channeling Precursor Supply 348 12.3.4.3 Eliminating Product Diversion 350 12.3.4.4 Enhancing Product Transport 350 12.3.4.5 Rerouting Pathways 350 12.3.5 Engineering Host Cells – Beyond the Pathway 352 12.3.5.1 Altering Substrate Utilization 352 12.3.5.2 Manipulating the Time Dynamics of Production 352 12.3.5.3 Increasing Product Tolerance 354 12.4 Pathway Synthesis 356 12.4.1 Host Cells: Native Hosts versus Archetypical Hosts 356 12.4.2 Expressing Heterologous Enzymes to Produce a Nonnative Product 357 12.4.3 Activating a Silent Pathway in a Native Host 359 12.5 Stoichiometric and Kinetic Considerations in Pathway Engineering 359 12.6 Synthetic Biology 367 12.6.1 Synthetic (Cell-Free) Biochemical Reaction System 367 12.6.2 Synthetic Circuits 369 12.6.2.1 Artificial Genetic Circuits 369 12.6.2.2 Synthetic Signaling Pathway 369 12.6.3 Synthetic Organisms 371 12.6.3.1 Minimum Genome and Reduced Genome 371 12.6.3.2 Chemical Synthesis of a Genome 372 12.6.3.3 Surrogate Cells from a Synthetic Genome 374 12.7 Concluding Remarks 374 Further Reading 374 Problems 375 13 Process Engineering of Bioproduct Recovery 381 13.1 Introduction 381 13.2 Characteristics of Biochemical Products 382 13.3 General Strategy of Bioproduct Recovery 385 13.3.1 Properties Used in Bioseparation 385 13.3.2 Stages in Bioseparation 387 13.3.2.1 Cell and Solid Removal 387 13.3.2.2 Product Isolation (Capture) and Volume Reduction 387 13.3.2.3 Product Purification 388 13.3.2.4 Product Polishing 388 13.4 Unit Operations in Bioseparation 389 13.4.1 Filtration 389 13.4.2 Centrifugation 390 13.4.3 Liquid–Liquid Extraction 393 13.4.4 Liquid Chromatography 395 13.4.5 Membrane Filtration 396 13.4.6 Precipitation and Crystallization 397 13.5 Examples of Industrial Bioseparation Processes 398 13.5.1 Recombinant Antibody IgG 398 13.5.2 Penicillin 401 13.5.3 Monosodium Glutamate 404 13.5.4 Cohn Fractionation 404 13.6 Concluding Remarks 404 Further Reading 406 Nomenclature 407 Problems 408 14 Chromatographic Operations in Bioseparation 413 14.1 Introduction 413 14.2 Adsorbent 415 14.2.1 Types of Adsorbent 415 14.2.2 Ligand and Mechanism of Separation 418 14.2.3 Types of Liquid Chromatography 419 14.3 Adsorption Isotherm 420 14.3.1 Adsorption Equilibrium: Langmuir Isotherm 420 14.3.2 Isotherm Dynamics in Adsorption and Desorption 421 14.4 Adsorption Chromatography 425 14.4.1 Discrete-Stage Analysis 425 14.4.2 Breakthrough Curve 427 14.4.3 An Empirical Two-Parameter Description of a Breakthrough Curve 429 14.4.4 One-Porosity Model for an Adsorption Process 431 14.4.5 Elution of Solutes from an Adsorption Column 433 14.5 Elution Chromatography 435 14.5.1 Discrete-Stage Analysis 435 14.5.2 Determination of Stage Number 441 14.5.3 Effect of Stage Number and Number of Theoretical Plates 442 14.5.4 Two-Porosity Model, Mass Transfer Limitation 444 14.6 Scale-Up and Continuous Operation 447 14.6.1 Mass Transfer Limitation and the van Deemter Equation 447 14.6.2 Scale-Up of Chromatography 448 14.6.3 Continuous Adsorption and Continuous Elution Chromatography 450 14.7 Concluding Remarks 454 Further Reading 454 Nomenclature 454 Problems 456 Index 471

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Book SynopsisFrom basic tenets to the latest advances, this is the most comprehensive and up-to-date coverage of the process of biodesulfurization in the petroleum refining industry. Petroleum refining and process engineering is constantly changing. No new refineries are being built, but companies all over the world are still expanding or re-purposing huge percentages of their refineries every year, year after year. Rather than building entirely new plants, companies are spending billions of dollars in the research and development of new processes that can save time and money by being more efficient and environmentally safer. Biodesulfurization is one of those processes, and nowhere else it is covered more thoroughly or with more up-to-date research of the new advances than in this new volume from Wiley-Scrivener. Besides the obvious benefits to biodesulfurization, there are new regulations in place within the industry with which companies will, over the next decade or longeTable of ContentsPreface xiii 1 Background 1 List of Abbreviations and Nomenclature 1 1.1 Petroleum 2 1.2 Petroleum Composition 7 1.2.1 Petroleum Hydrocarbons 8 1.2.2 Petroleum Non-Hydrocarbons 12 1.2.2.1 Problems Generated by Asphaltenes 14 1.3 Sulfur Compounds 15 1.4 Sulfur in Petroleum Major Refinery Products 20 1.4.1 Gasoline 20 1.4.2 Kerosene 23 1.4.3 Jet Fuel 23 1.4.4 Diesel Fuel 23 1.4.5 Heating/Fuel Oils 24 1.4.6 Bunker Oil 24 1.5 Sulfur Problem 25 1.6 Legislative Regulations of Sulfur Levels in Fuels 29 References 32 2 Desulfurization Technologies 39 List of Abbreviations and Nomenclature 39 2.1 Introduction 43 2.2 Hydrodesulfurization 47 2.3 Oxidative Desulfurization 71 2.4 Selective Adsorption 108 2.5 Biocatalytic Desulfurization 127 2.5.1 Anaerobic Process 127 2.5.2 Aerobic Process 128 References 130 3 Biodesulfurization of Natural Gas 159 List of Abbreviations and Nomenclature 159 3.1 Introduction 161 3.2 Natural Gas Processing 169 3.3 Desulfurization Processes 183 3.3.1 Scavengers 183 3.3.2 Adsorption 187 3.3.3 Liquid Redox Processes 193 3.3.4 Claus Plants 195 3.3.4.1 Classic Claus Plant 196 3.3.4.2 Split-Flow Claus Plant 198 3.3.4.3 Oxygen Enrichment Claus Plant 199 3.3.4.4 Claus Plant Tail Gas 199 3.3.5 Absorption/Desorption Process 201 3.3.6 Biodesulfurization 203 3.3.6.1 Photoautotrophic Bacteria 206 3.3.6.2 Heterotrophic Bacteria 211 3.3.6.3 Chemotrophic Bacteria 212 3.3.7 Other Approaches Concerning the Biodesulfurization of Natural Gas 231 References 242 4 Microbial Denitrogenation of Petroleum and its Fractions 263 List of Abbreviations and Nomenclature 263 4.1 Introduction 265 4.2 Denitrogenation of Petroleum and its Fractions 269 4.2.1 Hydrodenitrogenation 269 4.2.2 Adsorptive Denitrogenation 272 4.2.3 Extractive and Catalytic Oxidative Denitrogenation 278 4.3 Microbial Attack of Nitrogen Polyaromatic Heterocyclic Compounds (NPAHs) 279 4.4 Enhancing Biodegradation of NPAHs by Magnetic Nanoparticles 295 4.5 Challenges and Opportunities for BDN in Petroleum Industries 300 References 307 5 Bioadsorptive Desulfurization of Liquid Fuels 327 List of Abbreviations and Nomenclature 327 5.1 Introduction 329 5.2 ADS by Agroindustrial-Wastes Activated Carbon 332 5.3 ADS on Modified Activated Carbon 342 5.4 ADS on Carbon Aerogels 352 5.5 ADS on Activated Carbon Fibers 353 5.6 ADS on Natural Clay and Zeolites 355 5.7 ADS on New Adsorbents Prepared from Different Biowastes 360 References 365 6 Microbial Attack of Organosulfur Compounds 375 List of Abbreviations and Nomenclature 375 6.1 Introduction 377 6.2 Biodegradation of Sulfur Compounds in the Environment 380 6.3 Microbial Attack on Non–Heterocyclic Sulfur–Containing Hydrocarbons 383 6.3.1 Alkyl and Aryl Sulfides 383 6.3.2 Non – Aromatic Cyclic Sulfur – Containing Hydrocarbons 386 6.4 Microbial Attack of Heterocyclic Sulfur – Hydrocarbons 388 6.4.1 Thiophenes 389 6.4.2 Benzothiophenes and Alkyl-Substituted Benzothiophenes 390 6.4.3 Naphthothiophenes 402 6.4.4 Dibenzothiophene and Alkyl-Substituted Dibenzothiophenes 406 6.4.4.1 Aerobic Biodesulfurization of DBT 406 6.4.4.2 Aerobic Biodesulfurization of Alkylated DBT 419 6.4.4.3 Anaerobic Biodesulfurization of DBT 421 6.5 Recent Elucidated DBT-BDS Pathways 422 References 439 7 Enzymology and Genetics of Biodesulfurization Process 459 List of Abbreviations and Nomenclature 459 7.1 Introduction 461 7.2 Genetics of PASHs BDS Pathway 462 7.2.1 Anaerobic BDS Pathway 462 7.2.2 Aerobic BDS Pathway 463 7.2.2.1 Kodama Pathway 463 7.2.2.2 Complete Degradation Pathway 464 7.2.2.3 4S-Pathway 466 7.3 The Desulfurization dsz Genes 468 7.4 Enzymes Involved in Specific Desulfurization of Thiophenic Compounds 472 7.4.1 The Dsz Enzymes 472 7.4.1.1 DszC Enzyme (DBT-Monooxygenase) 474 7.4.1.2 DszA Enzyme (DBTO2-Monooxygenase) 476 7.4.1.3 DszB Enzyme (HBPS- Desulfinase) 477 7.4.1.4 DszD Enzyme (Flavin-Oxidoreductase Enzyme) 478 7.5 Repression of dsz Genes 480 7.6 Recombinant Biocatalysts for BDS 484 References 506 8 Factors Affecting the Biodesulfurization Process 521 List of Abbreviations and Nomenclature 521 8.1 Introduction 524 8.2 Effect of Incubation Period 525 8.3 Effect of Temperature and pH 527 8.4 Effect of Dissolved Oxygen Concentration 530 8.5 Effect of Agitation Speed 532 8.6 Effect of Initial Biomass Concentration 536 8.7 Effect of Biocatalyst Age 538 8.8 Effect of Mass Transfer 541 8.9 Effect of Surfactant 541 8.10 Effect of Initial Sulfur Concentration 544 8.11 Effect of Type of S-Compounds 546 8.12 Effect of Organic Solvent and Oil to Water Phase Ratio 553 8.13 Effect of Medium Composition 560 8.14 Effect of Growing and Resting Cells 579 8.15 Inhibitory Effect of Byproducts 580 8.16 Statistical Optimization 590 References 616 9 Kinetics of Batch Biodesulfurization Process 639 List of Abbreviations and Nomenclature 639 9.1 Introduction 642 9.2 General Background 643 9.2.1 Phases of Microbial Growth 643 9.2.1.1 The Lag Phase 644 9.2.1.2 The Log Phase 644 9.2.1.3 The Stationary Phase 645 9.2.1.4 The Decline Phase 645 9.2.2 Modeling of Population Growth as a Function of Incubation Time 645 9.3 Microbial Growth Kinetics 645 9.3.1 Exponential Growth Model 645 9.3.2 Logistic Growth Model 648 9.4 Some of the Classical Kinetic Models Applied in BDS-Studies 650 9.5 Factors Affecting the Rate of Microbial Growth 651 9.5.1 Effect of Temperature 651 9.5.2 Effect of pH 654 9.5.3 Effect of Oxygen 654 9.6 Enzyme Kinetics 654 9.6.1 Basic Enzyme Reactions 656 9.6.2 Factors Affecting the Enzyme Activity 657 9.6.2.1 Enzyme Concentration 657 9.6.2.2 Substrate Concentration 658 9.6.2.3 Effect of Inhibitors on Enzyme Activity 659 9.6.2.4 Effect of Temperature 660 9.6.2.5 Effect of pH 661 9.7 Michaelis-Menten Equation 662 9.7.1 Direct Integration Procedure 664 9.7.2 Lineweaver-Burk Plot Method 666 9.7.3 Eadie-Hofstee 666 9.8 Kinetics of a Multi-Substrates System 667 9.9 Traditional 4S-Pathway 668 9.9.1 Formulation of a Kinetic Model for DBT Desulfurization According to 4S-Pathway 669 9.10 Different Kinetic Studies on the Parameters Affecting the BDS Process 673 9.11 Evaluation of the Tested Biocatalysts 734 9.11.1 Kinetics of the Overall Biodesulfurization Reaction 735 9.11.2 Maximum Percentage of Desulfurization (XMAXBDS %) 735 9.11.3 Time for Maximum Biodesulfurization tBDSmax (min) 735 9.11.4 Initial DBT Removal Rate RODBT (μmol/L/min) 736 9.11.5 Maximum Productivity PMAXBDS (%/min) 736 9.11.6 Specific Conversion Rate (SE %L/g/min) 736 References 737 10 Enhancement of BDS Efficiency 753 List of Abbreviations and Nomenclature 753 10.1 Introduction 756 10.2 Isolation of Selective Biodesulfurizing Microorganisms with Broad Versatility on Different S-Compounds 757 10.2.1 Anaerobic Biodesulfurizing Microorganisms 758 10.2.2 Bacteria Capable of Aerobic Selective DBT-BDS 759 10.2.3 Microorganisms with Selective BDS of Benzothiophene and Dibenzothiophene 769 10.2.4 Microorganisms with Methoxylation Pathway 770 10.2.5 Microorganisms with High Tolerance for Oil/Water Phase Ratio 771 10.2.6 Thermotolerant Microorganisms with Selective BDS Capability 772 10.2.7 BDS Using Yeast and Fungi 776 10.3 Genetics and its Role in Improvement of BDS Process 778 10.4 Overcoming the Repression Effects of Byproducts 789 10.5 Enzymatic Oxidation of Organosulfur Compounds 793 10.6 Enhancement of Biodesulfurization via Immobilization 795 10.6.1 Types of Immobilization 800 10.6.1.1 Adsorption 800 10.6.1.2 Covalent Binding 809 10.6.1.3 Encapsulation 809 10.6.1.4 Entrapment 810 10.7 Application of Nano-Technology in BDS Process 826 10.8 Role of Analytical Techniques in BDS 849 10.8.1 Gas Chromatography 850 10.8.1.1 Determination of Sulfur Compounds by GC 850 10.8.1.2 Assessment of Biodegradation 851 10.8.2 Presumptive Screening for Desulfurization and Identification of BDS Pathway 852 10.8.2.1 Gibb’s Assay 853 10.8.2.2 Phenol Assay 853 10.8.3 More Advanced Screening for Desulfurization and Identification of BDS Pathway 854 10.8.3.1 High Performance Liquid Chromatography 854 10.8.3.2 X-ray Sulfur Meter and other Techniques for Determining Total Sulfur Content 855 References 857 11 Biodesulfurization of Real Oil Feed 895 List of Abbreviations and Nomenclature 895 11.1 Introduction 897 11.2 Biodesulfurization of Crude Oil 903 11.3 Biodesulfurization of Different Oil Distillates 909 11.4 BDS of Crude Oil and its Distillates by Thermophilic Microorganisms 921 11.5 Application of Yeast and Fungi in BDS of Real Oil Feed 923 11.6 Biocatalytic Oxidation 924 11.7 Anaerobic BDS of Real Oil Feed 926 11.8 Deep Desulfurization of Fuel Streams by Integrating Microbial with Non-Microbial Methods 928 11.8.1 BDS as a Complement to HDS 928 11.8.2 BDS as a Complementary to ADS 939 11.8.3 Coupling Non-Hydrodesulfurization with BDS 945 11.8.4 Three Step BDS-ODS-RADS 945 11.9 BDS of other Petroleum Products 946 References 952 12 Challenges and Opportunities 973 List of Abbreviations and Nomenclature 973 12.1 Introduction 975 12.2 New Strains with Broad Versatility 983 12.3 New Strains with Higher Hydrocarbon Tolerance 990 12.4 Overcoming the Feedback Inhibition of the End-Products 994 12.5 Biodesulfurization under Thermophilic Conditions 995 12.6 Anaerobic Biodesulfurization 997 12.7 Biocatalytic Oxidation 1000 12.8 Perspectives for Enhancing the Rate of BDS 1001 12.8.1 Application of Genetics in BDS 1002 12.8.2 Implementation of Resting Cells 1009 12.8.3 Microbial Consortium and BDS 1011 12.8.4 Surfactants and BDS 1014 12.8.5 Application of Nanotechnology in the BDS Process 1017 12.9 Production of Valuable Products 1028 12.10 Storage of Fuel and Sulfur 1031 12.11 Process Engineering Research 1033 12.12 BDS Process of Real Oil Feed 1053 12.13 BDS as a Complementary Technology 1061 12.14 Future Perspectives 1063 12.15 Techno-Economic Studies 1066 12.16 Economic Feasibility 1068 12.17 Fields of Developments 1077 12.18 BDS Now and Then 1080 12.19 Conclusion 1083 References 1084 Glossary 1119 Index 1155

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Book SynopsisA guide to all practical aspects of building, implementing, managing, and maintaining MPC applications in industrial plants Multivariable Predictive Control: Applications in Industry provides engineers with a thorough understanding of all practical aspects of multivariate predictive control (MPC) applications, as well as expert guidance on how to derive maximum benefit from those systems. Short on theory and long on step-by-step information, it covers everything plant process engineers and control engineers need to know about building, deploying, and managing MPC applications in their companies. MPC has more than proven itself to be one the most important tools for optimising plant operations on an ongoing basis. Companies, worldwide, across a range of industries are successfully using MPC systems to optimise materials and utility consumption, reduce waste, minimise pollution, and maximise production. Unfortunately, due in part to the lack of practical reTable of ContentsFigure List xix Table List xxi Preface xxiii 1 Introduction of Model Predictive Control 1 1.1 Purpose of Process Control in Chemical Process Industries (CPI) 1 1.2 Shortcomings of Simple Regulatory PID Control 2 1.3 What Is Multivariable Model Predictive Control? 3 1.4 Why Is a Multivariable Model Predictive Optimizing Controller Necessary? 4 1.5 Relevance of Multivariable Predictive Control (MPC) in Chemical Process Industry in Today’s Business Environment 6 1.6 Position of MPC in Control Hierarchy 6 1.6.1 Regulatory PID Control Layer 6 1.6.2 Advance Regulatory Control (ARC) Layer 8 1.6.3 Multivariable Model‐Based Control 8 1.6.4 Economic Optimization Layer 8 1.6.4.1 First Layer of Optimization 8 1.6.4.2 Second Layer of Optimization 9 1.6.4.3 Third Layer of Optimization 9 1.7 Advantage of Implementing MPC 10 1.8 How Does MPC Extract Benefit? 13 1.8.1 MPC Inherent Stabilization Effect 13 1.8.2 Process Interactions 14 1.8.3 Multiple Constraints 15 1.8.4 Intangible Benefits of MPC 17 1.9 Application of MPC in Oil Refinery, Petrochemical, Fertilizer, and Chemical Plants, and Related Benefits 17 2 Theoretical Base of MPC 23 2.1 Why MPC? 23 2.2 Variables Used in MPC 25 2.2.1 Manipulated Variables (MVs) 25 2.2.2 Controlled Variables (CVs) 25 2.2.3 Disturbance Variables (DVs) 25 2.3 Features of MPC 26 2.3.1 MPC Is a Multivariable Controller 26 2.3.2 MPC Is a Model Predictive Controller 26 2.3.3 MPC Is a Constrained Controller 26 2.3.4 MPC Is an Optimizing Controller 27 2.3.5 MPC Is a Rigorous Controller 27 2.4 Brief Introduction to Model Predictive Control Techniques 27 2.4.1 Simplified Dynamic Control Strategy of MPC 28 2.4.2 Step 1: Read Process Input and Output 29 2.4.3 Step 2: Prediction of CVs 30 2.4.3.1 Building Dynamic Process Model 30 2.4.3.2 How MPC Predicts the Future 32 2.4.4 Step 3: Model Reconciliation 33 2.4.5 Step 4: Determine the Size of the Control Process 34 2.4.6 Step 5: Removal of Ill‐Conditioned Problems 34 2.4.7 Step 6: Optimum Steady‐State Targets 35 2.4.8 Step 7: Develop Detailed Plan of MV Movement 36 3 Historical Development of Different MPC Technology 43 3.1 History of MPC Technology 43 3.1.1 Pre‐Era 43 3.1.1.1 Developer 43 3.1.1.2 Motivation 44 3.1.1.3 Limitations 44 3.1.2 First Generation of MPC (1970–1980) 44 3.1.2.1 Characteristics of First‐Generation MPC Technology 44 3.1.2.2 IDCOM Algorithm and Its Features 45 3.1.2.3 DMC Algorithm and Its Features 46 3.1.3 Second‐Generation MPC (1980–1985) 46 3.1.4 Third‐Generation MPC (1985–1990) 47 3.1.4.1 Distinguishing Features of Third‐Generation MPC Algorithm 48 3.1.4.2 Distinguishing Features of the IDCOM‐M Algorithm 49 3.1.4.3 Evolution of SMOC 50 3.1.4.4 Distinctive Features of SMOC 50 3.1.5 Fourth‐Generation MPC (1990–2000) 50 3.1.5.1 Distinctive Features of Fourth‐Generation MPC 51 3.1.6 Fifth‐Generation MPC (2000–2015) 51 3.2 Points to Consider While Selecting an MPC 52 4 MPC Implementation Steps 55 4.1 Implementing a MPC Controller 55 4.1.1 Step 1: Preliminary Cost–Benefit Analysis 55 4.1.2 Step 2: Assessment of Base Control Loops 55 4.1.3 Step 3: Functional Design of Controller 56 4.1.4 Step 4: Conduct the Preliminary Plant Test (Pre‐Stepping) 57 4.1.5 Step 5: Conduct the Plant Step Test 57 4.1.6 Step 6: Identify a Process Model 57 4.1.7 Step 7: Generate Online Soft Sensors or Virtual Sensors 58 4.1.8 Step 8: Perform Offline Controller Simulation/Tuning 58 4.1.9 Step 9: Commission the Online Controller 58 4.1.10 Step 10: Online MPC Controller Tuning 59 4.1.11 Step 11: Hold Formal Operator Training 59 4.1.12 Step 12: Performance Monitoring of MPC Controller 59 4.1.13 Step 13: Maintain the MPC Controller 60 4.2 Summary of Steps Involved in MPC Projects with Vendor 60 5 Cost–Benefit Analysis of MPC before Implementation 63 5.1 Purpose of Cost–Benefit Analysis of MPC before Implementation 63 5.2 Overview of Cost–Benefit Analysis Procedure 64 5.3 Detailed Benefit Estimation Procedures 65 5.3.1 Initial Screening for Suitability of Process to Implement MPC 65 5.3.2 Process Analysis and Economics Analysis 66 5.3.3 Understand the Constraints 67 5.3.4 Identify Qualitatively Potential Area of Opportunities 67 5.3.4.1 Example 1: Air Separation Plant 68 5.3.4.2 Example 2: Distillation Columns 69 5.3.5 Collect All Relevant Plant and Economic Data (Trends, Records) 69 5.3.6 Calculate the Standard Deviation and Define the Limit 69 5.3.7 Estimate the Stabilizing Effect of MPC and Shift in the Average 70 5.3.7.1 Benefit Estimation: When the Constraint Is Known 71 5.3.7.2 Benefit Estimation: When the Constraint Is Not Well Known or Changing 72 5.3.8 Estimate Change in Key Performance Parameters Such as Yield, Throughput, and Energy Consumption 72 5.3.8.1 Example: Ethylene Oxide Reactor 72 5.3.9 Identify How This Effect Translates to Plant Profit Margin 73 5.3.10 Estimate the Economic Value of the Effect 73 5.4 Case Studies 73 5.4.1 Case Study 1 73 5.4.1.1 Benefit Estimation Procedure 73 5.4.2 Case Study 2 74 5.4.2.1 Benefit Estimation Procedure 74 6 Assessment of Regulatory Base Control Layer in Plants 77 6.1 Failure Mode of Control Loops and Their Remedies 77 6.2 Control Valve Problems 77 6.2.1 Improper Valve Sizing 78 6.2.1.1 How to Detect a Particular Control Valve Sizing Problem 78 6.2.2 Valve Stiction 79 6.2.2.1 What Is Control Valve Stiction? 79 6.2.2.2 How to Detect Control Valve Stiction Online 80 6.2.2.3 Combating Stiction 80 6.2.2.4 Techniques for Combating Stiction Online 80 6.2.3 Valve Hysteresis and Backlash 81 6.3 Sensor Problems 82 6.3.1 Noisy 82 6.3.2 Flatlining 82 6.3.3 Scale/Range 82 6.3.4 Calibration 82 6.3.5 Overfiltered 83 6.4 Controller Problems 83 6.4.1 Poor Tuning and Lack of Maintenance 83 6.4.2 Poor or Missing Feedforward Compensation 83 6.4.3 Inappropriate Control Structure 84 6.5 Process‐Related Problems 84 6.5.1 Problems of Variable Gain 84 6.5.2 Oscillations 84 6.5.2.1 Variable Valve Gain 85 6.5.2.2 Variable Process Gain 85 6.6 Human Factor 85 6.7 Control Performance Assessment/Monitoring 86 6.7.1 Available Software for Control Performance Monitoring 86 6.7.2 Basic Assessment Procedure 87 6.8 Commonly Used Control System Performance KPIs 87 6.8.1 Traditional Indices 88 6.8.1.1 Peak Overshoot Ratio (POR) 88 6.8.1.2 Decay Rate 88 6.8.1.3 Peak Time and Rise Time 88 6.8.1.4 Settling Time 88 6.8.1.5 Integral of Error Indexes 88 6.8.2 Simple Statistical Indices 88 6.8.2.1 Mean of Control Error (%) 89 6.8.2.2 Standard Deviation of Control Error (%) 89 6.8.2.3 Standard Variation of Control Error (%) 89 6.8.2.4 Standard Deviation of Controller Output (%) 89 6.8.2.5 Skewness of Control Error 89 6.8.2.6 Kurtosis of Control Error 89 6.8.2.7 Ratio of Standard of Control Error and Controller Output 89 6.8.2.8 Maximum Bicoherence 90 6.8.3 Business/Operational Metrics 90 6.8.3.1 Loop Health 90 6.8.3.2 Service Factor 90 6.8.3.3 Key Performance Indicators 90 6.8.3.4 Operational Performance Efficiency Factor 90 6.8.3.5 Overall Loop Performance Index 90 6.8.3.6 Controller Output Changes in Manual 90 6.8.3.7 Mode Changes 90 6.8.3.8 Totalized Valve Reversals and Valve Travel 90 6.8.3.9 Process Model Parameters 90 6.8.4 Advanced Indices 90 6.8.4.1 Harris Index 91 6.8.4.2 Nonlinearity Index 91 6.8.4.3 Oscillation‐Detection Indices 91 6.8.4.4 Disturbance Detection Indices 92 6.8.4.5 Autocorrelation Indices 92 6.9 Tuning for PID Controllers 92 6.9.1 Complications with Tuning PID Controllers 93 6.9.2 Loop Retuning 93 6.9.3 Classical Controller Tuning Algorithms 94 6.9.3.1 Controller Tuning Methods 94 6.9.3.2 Ziegler‐Nichols Tuning Method 94 6.9.3.3 Dahlin (Lambda) Tuning Method 94 6.9.4 Manual Controller Tuning Methods in Absence of Any Software 95 6.9.4.1 Pre‐Tuning 95 6.9.4.2 Bring in Baseline Parameters 97 6.9.4.3 Some Like It Simple 97 6.9.4.4 Tuning Cascade Control 98 7 Functional Design of MPC Controllers 101 7.1 What Is Functional Design? 101 7.2 Steps in Functional Design 102 7.2.1 Step 1: Define Process Control Objectives 102 7.2.1.1 Economic Objectives 102 7.2.1.2 Operating Objectives 103 7.2.1.3 Control Objectives 104 7.2.2 Step 2: Identify Process Constraints 104 7.2.2.1 Process Limitations 104 7.2.2.2 Safety Limitations 104 7.2.2.3 Process Instrument Limitations 105 7.2.2.4 Raw Material and Utility Supply Limitation 105 7.2.2.5 Product Limitations 105 7.2.3 Step 3: Define Controller Scope 105 7.2.4 Step 4: Select the Variables 106 7.2.4.1 Economics of the Unit 106 7.2.4.2 Constraints of the Unit 107 7.2.4.3 Control of the Unit 107 7.2.4.4 Manipulated Variables (MVs) 107 7.2.4.5 Controlled Variables (CVs) 107 7.2.4.6 Disturbance Variables (DVs) 108 7.2.4.7 Practical Guidelines for Variable Selections 108 7.2.5 Step 5: Rectify Regulatory Control Issues 109 7.2.5.1 Practical Guidelines for Changing Regulatory Controller Strategy 109 7.2.6 Step 6: Explore the Scope of Inclusions of Inferential Calculations 110 7.2.7 Step 7: Evaluate Potential Optimization Opportunity 110 7.2.7.1 Practical Guidelines for Finding out Optimization Opportunities 111 7.2.8 Step 8: Define LP or QP Objective Function 111 7.2.8.1 CDU Example 112 8 Preliminary Process Test and Step Test 113 8.1 Pre‐Stepping, or Preliminary Process Test 113 8.1.1 What Is Pre‐Stepping? 113 8.1.2 Objective of Pre‐Stepping 113 8.1.3 Prerequisites of Pre‐Stepping 113 8.1.4 Pre‐Stepping 114 8.2 Step Testing 115 8.2.1 What Is a Step Test? 115 8.2.2 What Is the Purpose of a Step Test? 115 8.2.3 Details of Step Testing 116 8.2.3.1 Administrative Aspects 116 8.2.3.2 Technical Aspects 116 8.2.4 Different Step‐Testing Method 117 8.2.4.1 Manual Step Testing 117 8.2.4.2 PRBS (Pseudo Random Binary Sequence) 117 8.2.4.3 General Guidelines of PRBS Test 117 8.2.5 Difference between Normal Step Testing and PRBS Testing 118 8.2.6 Which One to Choose? 118 8.2.7 Dos and Don’ts of Step Testing 118 8.3 Development of Step‐Testing Methodology over the Years 120 9 Model Building and System Identification 123 9.1 Introduction to Model Building 123 9.2 Key Issues in Model Identifications 124 9.2.1 Identification Test 124 9.2.2 Model Structure and Parameter Estimation 125 9.2.3 Order Selection 126 9.2.4 Model Validation 127 9.3 The Basic Steps of System Identification 127 9.3.1 Step 0: Experimental Design and Execution 128 9.3.2 Step 1: Plan the Case that Needs to Be Modeled 130 9.3.2.1 Action 1 130 9.3.2.2 Action 2 130 9.3.3 Step 2: Identify Good Slices of Data 130 9.3.3.1 Looking at the Data 131 9.3.4 Step 3: Pre‐Processing of Data 131 9.3.5 Step 4: Identification of Model Curve 132 9.3.5.1 Hybrid Approach to System Identification 132 9.3.5.2 Direct Modeling Approach of System Identification 133 9.3.5.3 Subspace Identification 134 9.3.5.4 Detailed Steps of Implementations 135 9.3.6 Step 5: Select Final Model 136 9.4 Model Structures 137 9.4.1 FIR Models 138 9.4.1.1 FIR Structures 138 9.4.2 Prediction Error Models (PEM Models) 139 9.4.2.1 PEM Structures 139 9.4.3 Model for Order and Variance Reduction 140 9.4.3.1 ARX Parametric Models (Discrete Time) 140 9.4.3.2 Output Error Models (Discrete Time) 140 9.4.3.3 Laplace Domain Parametric Models 141 9.4.3.4 Final Model Form 141 9.4.4 State‐Space Models 141 9.4.5 How to Know Which Structure and Method to Use 142 9.5 Common Features of Commercial Identification Packages 142 10 Soft Sensors 145 10.1 What Is a Soft Sensor? 145 10.2 Why Soft Sensors Are Necessary 145 10.2.1 Process Monitoring and Process Fault Detection 146 10.2.2 Sensor Fault Detection and Reconstruction 146 10.2.3 Use of Soft Sensors in MPC Application 146 10.3 Types of Soft Sensors 147 10.3.1 First Principle‐Based Soft Sensors 147 10.3.1.1 Advantages 147 10.3.1.2 Disadvantages 147 10.3.2 Data‐Driven Soft Sensors 148 10.3.2.1 Advantages 148 10.3.2.2 Disadvantages 148 10.3.3 Gray Model‐Based Soft Sensors 148 10.3.3.1 Advantages 149 10.3.4 Hybrid Model‐Based Soft Sensors 149 10.3.4.1 Advantages 149 10.4 Soft Sensors Development Methodology 149 10.4.1 Data Collection and Data Inspection 149 10.4.2 Data Preprocessing and Data Conditioning 150 10.4.2.1 Outlier Detection and Replacement 151 10.4.2.2 Univariate Approach to Detect Outliers 151 10.4.2.3 Multivariate Approach to Detect Outliers (Lin 2007) 151 10.4.2.4 Handling of Missing Data 152 10.4.3 Selection of Relevant Input Output Variables 153 10.4.4 Data Alignment 153 10.4.5 Model Selection, Training, and Validation (Kadlec 2009; Lin 2007) 153 10.4.6 Analyze Process Dynamics 154 10.4.7 Deployment and Maintenance 155 10.5 Data‐Driven Methods for Soft Sensing 156 10.5.1 Principle Component Analysis 156 10.5.1.1 The Basics of PCA 156 10.5.1.2 Why Do We Need to Rotate the Data? 156 10.5.1.3 How Do We Generate Principal Components? 156 10.5.1.4 Steps to Calculating Principal Components 157 10.5.2 Partial Least Squares 157 10.5.3 Artificial Neural Networks 158 10.5.3.1 Network Architecture 159 10.5.3.2 Back Propagation Algorithm (BPA) 159 10.5.4 Neuro‐Fuzzy Systems 160 10.5.5 Support Vector Machines 161 10.5.5.1 Support Vector Regression–Based Modeling 161 10.6 Open Issues and Future Steps of Soft Sensor Development 162 10.6.1 Large Effort Required for Preprocessing of Industrial Data 162 10.6.2 Which Modeling Method to Choose? 163 10.6.3 Agreement of the Developed Model with Physics of the Process 163 10.6.4 Performance Deterioration of Developed Soft Sensor Model 163 11 Offline Simulation 167 11.1 What Is Offline Simulation? 167 11.2 Purpose of Offline Simulation 167 11.3 Main Task of Offline Simulation 168 11.4 Understanding Different Tuning Parameters of Offline Simulations 168 11.4.1 Tuning Parameters for CVs 169 11.4.1.1 Methods for Handling of Infeasibility 170 11.4.1.2 Priority Ranking of CVs 170 11.4.1.3 cv Give‐Up 170 11.4.1.4 cv Error Weight 170 11.4.2 Tuning Parameters for MVs 171 11.4.2.1 mv Maximum Movement Limits or Rate‐of‐Change Limits 171 11.4.2.2 Movement Weights 171 11.4.3 Tuning Parameters for Optimizer 172 11.4.3.1 Economic Optimization 172 11.4.3.2 General Form of Objective Function 173 11.4.3.3 Weighting Coefficients 173 11.4.3.4 Setting Linear Objective Coefficients 173 11.4.3.5 Optimization Horizon and Optimization Speed Factor 174 11.4.3.6 Optimization Speed Factor 174 11.4.3.7 mv Optimization Priority 174 11.4.4 Soft Limits 175 11.4.4.1 How Soft Limits Work 175 11.4.4.2 cv Soft Limits 175 11.4.4.3 mv Soft Limits 176 11.5 Different Steps to Build and Activate Simulator in an Offline PC 176 11.6 Example of Tests Carried out in Simulator 177 11.6.1 Control and Optimization Objectives 177 11.6.1.1 Test 1 178 11.6.1.2 Test 2 179 11.6.1.3 Test 3 179 11.6.1.4 Test 4 180 11.6.1.5 Test 5 180 11.6.1.6 Test 6 180 11.6.1.7 Others Tests 181 11.7 Guidelines for Choosing Tuning Parameters 181 11.7.1 Guidelines for Choosing Initial Values 181 11.7.2 How to Select Maximum Move Size and MV Movement Weights During Simulation Study 182 12 Online Deployment of MPC Application in Real Plants 183 12.1 What Is Online Deployment (Controller Commissioning)? 183 12.2 Steps for Controller Commissioning 183 12.2.1 Set up the Controller Configuration and Final Review of the Model 183 12.2.2 Build the Controller 184 12.2.3 Load Operator Station on PC Near the Panel Operator 184 12.2.4 Take MPC Controller in Line with Prediction Mode 186 12.2.5 Put the MPC Controller in Close Loop with One CV at a Time 187 12.2.6 Observe MPC Controller Performance 187 12.2.7 Put Optimizer in Line and Observe Optimizer Performance 189 12.2.8 Evaluate Overall Controller Performance 189 12.2.9 Perform Online Tuning and Troubleshooting 190 12.2.10 Train Operators and Engineers on Online Platform 190 12.2.11 Document MPC Features 190 12.2.12 Maintain the MPC Controller 191 13 Online Controller Tuning 193 13.1 What Is Online MPC Controller Tuning? 193 13.2 Basics of Online Tuning 193 13.2.1 Key Checkout Regarding Controller Performance 193 13.2.2 Steps to Troubleshoot the Problem 194 13.3 Guidelines to Choose Different Tuning Parameters 195 14 Why Do Some MPC Applications Fail? 199 14.1 What Went Wrong? 199 14.2 Failure to Build Efficient MPC Application 201 14.2.1 Historical Perspective 201 14.2.2 Capability of MPC Software to Capture Benefits 202 14.2.3 Expertise of Implementation Team 202 14.2.3.1 MPC Vendor Limitations 203 14.2.3.2 Client Limitations 204 14.2.4 Reliability of APC Project Methodology 204 14.3 Contributing Failure Factors of Postimplementation MPC Application 205 14.3.1 Technical Failure Factors 206 14.3.1.1 Lack of Performance Monitoring of MPC Application 206 14.3.1.2 Unresolved Basic Control Problems 206 14.3.1.3 Poor Tuning and Degraded Model Quality 207 14.3.1.4 Problems Related to Controller Design 207 14.3.1.5 Significant Process Modifications and Enhancement 207 14.3.2 Nontechnical Failure Factors 208 14.3.2.1 Lack of Properly Trained Personnel 208 14.3.2.2 Lack of Standards and Guidelines to MPC Support Personnel 208 14.3.2.3 Lack of Organizational Collaboration and Alignment 208 14.3.2.4 Poor Management of Control System 209 14.4 Strategies to Avoid MPC Failures 210 14.4.1 Technical Solutions 211 14.4.1.1 Development of Online Performance Monitoring of APC Applications 211 14.4.1.2 Improvement of Base Control Layer 212 14.4.1.3 Tuning Basic Controls 212 14.4.1.4 Control Performance Monitoring Software 213 14.4.2 Management Solutions 214 14.4.2.1 Training of MPC Console Operators 214 14.4.2.2 Training of MPC Control Engineers 215 14.4.2.3 Development of Corporate MPC Standards and Guidelines 216 14.4.2.4 Central Engineering Support Organization for MPC 217 14.4.3 Outsourcing Solutions 219 15 MPC Performance Monitoring 221 15.1 Why Performance Assessment of MPC Application Is Necessary 221 15.2 Types of Performance Assessment 222 15.2.1 Control Performance 222 15.2.2 Optimization Performance 222 15.2.3 Economic Performance 222 15.2.4 Intangible Performance 222 15.3 Benefit Measurement after MPC Implementation 222 15.4 Parameters to Be Monitored for MPC Performance Evaluation 223 15.4.1 Service Factors 224 15.4.2 KPI for Financial Criteria 224 15.4.3 KPI for Standard Deviation of Key Process Variable 225 15.4.3.1 Safety Parameters 225 15.4.3.2 Quality Giveaway Parameters 225 15.4.3.3 Economic Parameters 225 15.4.4 KPI for Constraint Activity 226 15.4.5 KPI for Constraint Violation 226 15.4.6 KPI for Inferential Model Monitoring 226 15.4.7 Model Quality 226 15.4.8 Limit Change Frequencies for CV/MVs 227 15.4.9 Active MV Limit 227 15.4.10 Long‐Term Performance Monitoring of MPC 227 15.5 KPIs to Troubleshoot Poor Performance of Multivariable Controls 228 15.5.1 Supporting KPIs for Low Service Factor 228 15.5.2 KPIs to Troubleshoot Cycling 229 15.5.3 KPIs for Oscillation Detection 230 15.5.4 KPIs for Regulatory Control Issues 230 15.5.5 KPIs for Measuring Operator Actions 231 15.5.6 KPIs for Measuring Process Changes and Disturbances 231 15.6 Exploitation of Constraints Handling and Maximization of MPC Benefit 231 16 Commercial MPC Vendors and Applications 235 16.1 Basic Modules and Components of Commercial MPC Software 235 16.1.1 Basic MPC Package 235 16.1.2 Data Collection Module 236 16.1.3 MPC Online Controller 236 16.1.4 Operator/ Engineer Station 237 16.1.5 System Identification Module 237 16.1.5.1 Different Modeling Options 239 16.1.5.2 Reporting and Documentation Function 239 16.1.5.3 Data Analysis and Pre‐Processing 239 16.1.6 PC‐Based Offline Simulation Package 240 16.1.7 Control Performance Monitoring and Diagnostics Software 240 16.1.7.1 Control Performance Monitoring 240 16.1.7.2 Basic Features of Performance Monitoring and Diagnostics Software 240 16.1.7.3 Performance and Benefits Metrics 241 16.1.7.4 Offline Module 241 16.1.7.5 Online Package 241 16.1.7.6 Online Reports 241 16.1.8 Soft Sensor Module (Also Called Quality Estimator Module) 242 16.1.8.1 Soft Sensor Offline Package 242 16.1.8.2 Soft Sensor Online Package 243 16.1.8.3 Soft Sensor Module Simulation Tool 243 16.2 Major Commercial MPC Software 243 16.3 AspenTech and DMCplus 244 16.3.1 Brief History of Development 244 16.3.1.1 Enhancement of DMC Technology to QDMC Technology in 1983, Regarded as Second‐Generation of MPC Technology (1980–1985) 244 16.3.1.2 Introduction of AspenTech and Evolvement of Third‐Generation MPC Technology (1985–1990) 245 16.3.1.3 Appearance of DMCplus Product with Fourth‐Generation MPC Technology (1990–2000) 245 16.3.1.4 Improvement of DMCplus Technology for Quicker Implementation in Shop Floor, Regarded as Fifth‐Generation MPC (2000–2015) 245 16.3.2 DMCplus Product Package 246 16.3.2.1 Aspen DMCplus Desktop 246 16.3.2.2 Aspen DMCplus Online 246 16.3.2.3 DMCplus Models and Identification Package 247 16.3.2.4 Aspen IQ (Soft Sensor Software) 247 16.3.2.5 Aspen Watch: AspenTech MPC Monitoring and Diagnostic Software 247 16.3.3 Distinctive Features of DMCplus Software Package 248 16.3.3.1 Automating Best Practices in Process Unit Step Testing 248 16.3.3.2 Adaptive Modeling 248 16.3.3.3 New Innovation 249 16.3.3.4 Background Step Testing 250 16.4 RMPCT by Honeywell 251 16.4.1 Brief History of Development 251 16.4.2 Honeywell MPC Product Package and Its Special Features 251 16.4.3 Key Features and Functions of RMPCT 251 16.4.3.1 Special Feature to Handle Model Error 251 16.4.3.2 Coping with Model Error 252 16.4.3.3 Funnels 252 16.4.3.4 Range Control Algorithm 252 16.4.4 Product Value Optimization Capabilities 252 16.4.5 “One‐Knob” Tuning 253 16.5 SMOC–Shell Global Solution 253 16.5.1 Evolution of Advance Process Control in Shell 253 16.5.1.1 1975–1998: The Beginnings 253 16.5.1.2 1998–2008: Shell Global Solution and Partnering with Yokogawa Era 254 16.5.1.3 2008 Onward: Shell Returns to Its Own Application 254 16.5.2 Shell MPC Product Package and Its Special Features 255 16.5.2.1 Key Characteristics of SMOC 255 16.5.2.2 Applications 255 16.5.3 SMOC Integrated Software Modules 255 16.5.3.1 AIDA Pro Offline Modeling Package 256 16.5.3.2 md Pro 256 16.5.3.3 RQE Pro 256 16.5.3.4 SMOC Pro 257 16.5.4 SMOC Claim of Superior Distinctive Features 259 16.5.4.1 Integrated Dynamic Modeling Tools and Automatic Step Tests 259 16.5.4.2 State‐of‐the‐Art Online Commissioning Tools 259 16.5.4.3 Online Tuning 259 16.5.4.4 Advance Regulatory Controls 260 16.5.4.5 Features of New Product 260 16.6 Conclusion 261 Index 263

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Book SynopsisThe revised edition of the guide to environmental impact of pharmaceuticals and personal care products The revised and updated second edition of Pharma-Ecologyjoins the health and environmental sciences professions'' concern over the occurrence and fate of pharmaceutical and personal care products (PPCPs) in the environment and explores how to best minimize their impact. The text highlights the biological effects of various classes of pharmaceutical compounds under clinical settings, their modes of action, and approximate quantities consumed. The second edition contains the most recent knowledge about the ecological impact of PPCPs as more sensitive detection techniques have become available, since the book was first published. The second edition offers the most up-to-date information on pharma ecology and bridges the gap between medicine, public health, and environmental science. This new edition contains helpful learning objectives for each chapter, as Table of ContentsPreface ix 1 Usage of Pharmaceutical and Personal Care Products 1 1.1 Pharmaceutical Consumption Trends 9 Study Questions 11 References 12 2 Most Prescribed Pharmaceuticals and Related Endpoints 15 2.1 Antihypertensive and Cardiovascular 16 2.2 Anxiolytic Sedatives, Hypnotics, and Antipsychotics 21 2.3 Analgesics and Anti‐inflammatory Drugs 29 Study Questions 33 References 33 3 Usage of Antimicrobial Agents and Related Endpoints 39 3.1 Cell Wall Synthesis Inhibiting Antibiotics 41 3.2 Inhibitors of Protein Synthesis 46 3.3 Nucleic Acid Synthesis Inhibitors 60 3.4 Antagonism to Metabolic Processes 67 3.5 Antibiotics that Disrupt Membrane Integrity 68 3.6 Other Antimicrobials 69 Study Questions 70 References 70 4 Usage of Other Groups of Pharmaceuticals and Related Endpoints 75 4.1 Gastrointestinal Drugs 76 4.2 Antidiabetic Drugs 78 4.3 Diuretics and Electrolytes 79 4.4 Thyroid System Medication 81 4.5 Respiratory Drugs 82 4.6 Oral Contraceptive and Reproductive Therapeutics 84 4.7 Biophosphonates and Other Skeletal Ailment Drugs 90 4.8 Steroids 91 4.9 Hematologic Drugs 94 4.10 Nutritional Drugs 94 4.11 Triptans 95 4.12 Anesthetics 96 4.13 Antineoplastics and Immunosuppressants 97 Study Questions 98 References 98 5 Personal Care Products of Environmental Concern 103 5.1 Fragrances and Musks 104 5.2 Ultraviolet Light Filters 111 5.3 Detergents 111 5.4 Disinfectants 114 Study Questions 115 References 116 6 Detection and Occurrence of PPCPs in the Environment 119 6.1 Detection of PPCPs in the Environment 123 6.1.1 Detection Using Instrumentation 126 6.1.2 Detection Using Bioassays 127 6.2 Occurrence of PPCPs in Various Environments 131 6.2.1 Aquatic Systems 133 6.2.1.1 PPCPs in Wastewater 133 6.2.1.2 PPCPs in Surface Water 141 6.2.1.3 PPCPs in Groundwater 146 6.2.1.4 PPCPs in Potable Water 149 6.2.2 Occurrence of PPCPs in Sediments 152 6.2.3 Occurrence of PPCPs in Soil 152 6.2.4 PPCPs in Aerial Environments 154 6.3 Excretion as a Driver of Pharmaceutical Occurrence in the Environment 158 Study Questions 162 References 163 7 Ecopharmacokinetics and Ecopharmacodynamics of PPCPs 177 7.1 Overview of Pharmacokinetics and Pharmacodynamics 178 7.1.1 PPCP Sorption and Bioavailability in the Environment 188 7.1.2 Compound Half‐life and Clearance 192 7.2 Degradation of PPCPs in the Environment 196 7.2.1 Degradation of Antibiotics in the Environment 197 7.2.1.1 Degradation of Quinolone Compounds 198 7.2.1.2 Fate of β‐Lactams and Cephalosporins 199 7.2.1.3 Degradation of Tetracyclines 201 7.2.1.4 Degradation of Macrolides 203 7.2.1.5 Fate of Other Important Groups of Antibiotics 203 7.2.2 Degradation of Analgesics and Anti‐inflammatory Drugs 204 7.2.3 Degradation of Estrogens and Other Reproductive Hormones 207 7.2.4 Degradation of Other Important Pharmaceuticals 210 7.2.5 Degradation of Surfactants 210 7.3 Role of Physicochemical Factors in the Fate of PPCPs in the Environment 211 7.3.1 Molecular Size as an Attribute to Absorption and Persistence 211 7.3.2 Solubility and Hydrolysis 212 7.3.3 Effects of Dissociation, Partitioning, and Lipophilicity on Degradability 214 7.3.4 Effects of Moisture and Oxygen to the Fate of PPCPs in the Environment 217 7.3.5 Effects of Temperature in PPCP Dynamics and Degradation in the Environment 218 7.3.6 Other Determinants of PPCP Fate and Persistence in the Environment 219 7.3.6.1 Presence of Other Compounds 219 7.3.6.2 Photolysis of PPCPs 221 Study Questions 225 References 226 8 Ecotoxicity of Pharmaceuticals and Personal Care Products 239 8.1 Conventional Assessment of the Risk 245 8.2 Ecological Impact of PPCPs on Microorganisms and Microbial Processes 250 8.2.1 Antibiotic Resistance 250 8.2.1.1 Acquisition of Antibiotic Resistance 256 8.2.1.2 Mechanisms of Antibiotic Resistance 256 8.2.2 Biogeochemical Perturbations 257 8.3 Effects of PPCPs on Invertebrates 259 8.4 PPCP Ecotoxicity on Aquatic Organisms 261 8.4.1 Endocrine Disrupters in the Aquatic System 264 8.4.2 Effects of Antibiotic Resistance to Aquatic Organisms 269 8.4.3 Ecotoxicological Effects of Cosmetics on Aquatic Organisms 269 8.4.4 Ecotoxicity of Other PPCPs in Aquatic Organisms 270 8.5 Ecotoxicity of PPCPs on Terrestrial Wildlife 272 8.6 Livestock and Human Health 276 8.6.1 Clinical Antibiotic‐resistance Cases 277 8.6.2 PPCP‐related Allergic Reactions 282 8.6.3 Endocrine Disruption in Humans and Livestock 283 8.6.4 Is There an Association Between PPCPs in the Environment and Some Cancers? 284 8.6.5 Other PPCPs of Concern to Humans and Livestock in the Environment 286 8.7 Ecotoxicity of PPCPs on Vegetation 286 8.8 General Considerations in Long‐term PPCP Toxicity 287 Study Questions 289 References 290 9 Technologies for Removing and Reducing PPCPs in the Environment 313 9.1 Conventional Treatment Systems 316 9.1.1 Primary Treatment 316 9.1.2 Secondary Treatment 317 9.1.2.1 Lagoons 317 9.1.2.2 Fixed Filter Systems 318 9.1.2.3 Suspended Filter Systems 319 9.2 Advanced Treatment Processes 320 9.2.1 Advanced Filtration Systems 321 9.2.1.1 Activated Carbon 321 9.2.1.2 Filtration Membranes 328 9.2.2 Oxidation Processes 338 9.2.2.1 Chlorination 338 9.2.2.2 Ozonation 340 9.2.3 UV Treatment 342 9.2.4 Electrolysis 342 9.2.5 Advanced Oxidation Processes 344 9.3 Effect of Wastewater Retention Time on PPCP Removal 346 9.4 Formulation and Regimen Design for Reduced Environmental Impact 347 9.5 Source Separation of Urine and Decentralization Needs 348 9.6 Future Technological Trends 348 Study Questions 349 References 350 10 Guidelines for a Regulatory Framework on PPCPs in the Environment 357 10.1 Improving Assessment of the Risks from PPCPs in the Environment 359 10.2 Effect of Mixtures 363 10.3 Effects of Chronic Exposure to Low PPCP Doses 363 10.4 Use of Quantitative Structure–Activity Relationships in Ecotoxicology 364 10.5 Toxicogenomic Approaches for Guiding Regulations 365 10.6 Social Responsibility in Legislation and Making Policy 366 10.7 Drug Approval and Advertising 371 10.8 Use of Prescription Records for Mapping PPCPs 372 Study Questions 373 References 374 Index 377

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Book SynopsisThe first statistics guide focussing on practical application to process control design and maintenance Statistics for Process Control Engineers is the only guide to statistics written by and for process control professionals. It takes a wholly practical approach to the subject.Table of ContentsPreface xiii About the Author xix Supplementary Material xxi Part 1: The Basics 1 1. Introduction 3 2. Application to Process Control 5 2.1 Benefit Estimation 5 2.2 Inferential Properties 7 2.3 Controller Performance Monitoring 7 2.4 Event Analysis 8 2.5 Time Series Analysis 9 3. Process Examples 11 3.1 Debutaniser 11 3.2 De-ethaniser 11 3.3 LPG Splitter 12 3.4 Propane Cargoes 17 3.5 Diesel Quality 17 3.6 Fuel Gas Heating Value 18 3.7 Stock Level 19 3.8 Batch Blending 22 4. Characteristics of Data 23 4.1 Data Types 23 4.2 Memory 24 4.3 Use of Historical Data 24 4.4 Central Value 25 4.5 Dispersion 32 4.6 Mode 33 4.7 Standard Deviation 35 4.8 Skewness and Kurtosis 37 4.9 Correlation 46 4.10 Data Conditioning 47 5. Probability Density Function 51 5.1 Uniform Distribution 55 5.2 Triangular Distribution 57 5.3 Normal Distribution 59 5.4 Bivariate Normal Distribution 62 5.5 Central Limit Theorem 65 5.6 Generating a Normal Distribution 69 5.7 Quantile Function 70 5.8 Location and Scale 71 5.9 Mixture Distribution 73 5.10 Combined Distribution 73 5.11 Compound Distribution 75 5.12 Generalised Distribution 75 5.13 Inverse Distribution 76 5.14 Transformed Distribution 76 5.15 Truncated Distribution 77 5.16 Rectified Distribution 78 5.17 Noncentral Distribution 78 5.18 Odds 79 5.19 Entropy 80 6. Presenting the Data 83 6.1 Box and Whisker Diagram 83 6.2 Histogram 84 6.3 Kernel Density Estimation 90 6.4 Circular Plots 95 6.5 Parallel Coordinates 97 6.6 Pie Chart 98 6.7 Quantile Plot 98 7. Sample Size 105 7.1 Mean 105 7.2 Standard Deviation 106 7.3 Skewness and Kurtosis 107 7.4 Dichotomous Data 108 7.5 Bootstrapping 110 8. Significance Testing 113 8.1 Null Hypothesis 113 8.2 Confidence Interval 116 8.3 Six-Sigma 118 8.4 Outliers 119 8.5 Repeatability 120 8.6 Reproducibility 121 8.7 Accuracy 122 8.8 Instrumentation Error 123 9. Fitting a Distribution 127 9.1 Accuracy of Mean and Standard Deviation 130 9.2 Fitting a CDF 131 9.3 Fitting a QF 134 9.4 Fitting a PDF 135 9.5 Fitting to a Histogram 138 9.6 Choice of Penalty Function 141 10. Distribution of Dependent Variables 147 10.1 Addition and Subtraction 147 10.2 Division and Multiplication 148 10.3 Reciprocal 153 10.4 Logarithmic and Exponential Functions 153 10.5 Root Mean Square 162 10.6 Trigonometric Functions 164 11. Commonly Used Functions 165 11.1 Euler’s Number 165 11.2 Euler–Mascheroni Constant 166 11.3 Logit Function 166 11.4 Logistic Function 167 11.5 Gamma Function 168 11.6 Beta Function 174 11.7 Pochhammer Symbol 174 11.8 Bessel Function 176 11.9 Marcum Q-Function 178 11.10 Riemann Zeta Function 180 11.11 Harmonic Number 180 11.12 Stirling Approximation 182 11.13 Derivatives 183 12. Selected Distributions 185 12.1 Lognormal 186 12.2 Burr 189 12.3 Beta 191 12.4 Hosking 195 12.5 Student t 204 12.6 Fisher 208 12.7 Exponential 210 12.8 Weibull 213 12.9 Chi-Squared 216 12.10 Gamma 221 12.11 Binomial 225 12.12 Poisson 231 13. Extreme Value Analysis 235 14. Hazard Function 245 15. Cusum 253 16. Regression Analysis 259 16.1 F Test 275 16.2 Adjusted R 2 278 16.3 Akaike Information Criterion 279 16.4 Artificial Neural Networks 281 16.5 Performance Index 286 17. Autocorrelation 291 18. Data Reconciliation 299 19. Fourier Transform 305 Part 2: Catalogue of Distributions 315 20. Normal Distribution 317 20.1 Skew-Normal 317 20.2 Gibrat 320 20.3 Power Lognormal 320 20.4 Logit-Normal 321 20.5 Folded Normal 321 20.6 Lévy 323 20.7 Inverse Gaussian 325 20.8 Generalised Inverse Gaussian 329 20.9 Normal Inverse Gaussian 330 20.10 Reciprocal Inverse Gaussian 332 20.11 Q-Gaussian 334 20.12 Generalised Normal 338 20.13 Exponentially Modified Gaussian 345 20.14 Moyal 347 21. Burr Distribution 349 21.1 Type I 349 21.2 Type II 349 21.3 Type III 349 21.4 Type IV 350 21.5 Type V 351 21.6 Type VI 351 21.7 Type VII 353 21.8 Type VIII 354 21.9 Type IX 354 21.10 Type X 355 21.11 Type XI 356 21.12 Type XII 356 21.13 Inverse 357 22. Logistic Distribution 361 22.1 Logistic 361 22.2 Half-Logistic 364 22.3 Skew-Logistic 365 22.4 Log-Logistic 367 22.5 Paralogistic 369 22.6 Inverse Paralogistic 370 22.7 Generalised Logistic 371 22.8 Generalised Log-Logistic 375 22.9 Exponentiated Kumaraswamy–Dagum 376 23. Pareto Distribution 377 23.1 Pareto Type I 377 23.2 Bounded Pareto Type I 378 23.3 Pareto Type II 379 23.4 Lomax 381 23.5 Inverse Pareto 381 23.6 Pareto Type III 382 23.7 Pareto Type IV 383 23.8 Generalised Pareto 383 23.9 Pareto Principle 385 24. Stoppa Distribution 389 24.1 Type I 389 24.2 Type II 389 24.3 Type III 391 24.4 Type IV 391 24.5 Type V 392 25. Beta Distribution 393 25.1 Arcsine 393 25.2 Wigner Semicircle 394 25.3 Balding–Nichols 395 25.4 Generalised Beta 396 25.5 Beta Type II 396 25.6 Generalised Beta Prime 399 25.7 Beta Type IV 400 25.8 Pert 401 25.9 Beta Rectangular 403 25.10 Kumaraswamy 404 25.11 Noncentral Beta 407 26. Johnson Distribution 409 26.1 S N 409 26.2 S U 410 26.3 S l 412 26.4 S B 412 26.5 Summary 413 27. Pearson Distribution 415 27.1 Type I 416 27.2 Type II 416 27.3 Type III 417 27.4 Type IV 418 27.5 Type V 424 27.6 Type VI 425 27.7 Type VII 429 27.8 Type VIII 433 27.9 Type IX 433 27.10 Type X 433 27.11 Type XI 434 27.12 Type XII 434 28. Exponential Distribution 435 28.1 Generalised Exponential 435 28.2 Gompertz–Verhulst 435 28.3 Hyperexponential 436 28.4 Hypoexponential 437 28.5 Double Exponential 438 28.6 Inverse Exponential 439 28.7 Maxwell–Jüttner 439 28.8 Stretched Exponential 440 28.9 Exponential Logarithmic 441 28.10 Logistic Exponential 442 28.11 Q-Exponential 442 28.12 Benktander 445 29. Weibull Distribution 447 29.1 Nukiyama–Tanasawa 447 29.2 Q-Weibull 447 30. Chi Distribution 451 30.1 Half-Normal 451 30.2 Rayleigh 452 30.3 Inverse Rayleigh 454 30.4 Maxwell 454 30.5 Inverse Chi 458 30.6 Inverse Chi-Squared 459 30.7 Noncentral Chi-Squared 460 31. Gamma Distribution 463 31.1 Inverse Gamma 463 31.2 Log-Gamma 463 31.3 Generalised Gamma 467 31.4 Q-Gamma 468 32. Symmetrical Distributions 471 32.1 Anglit 471 32.2 Bates 472 32.3 Irwin–Hall 473 32.4 Hyperbolic Secant 475 32.5 Arctangent 476 32.6 Kappa 477 32.7 Laplace 478 32.8 Raised Cosine 479 32.9 Cardioid 481 32.10 Slash 481 32.11 Tukey Lambda 483 32.12 Von Mises 486 33. Asymmetrical Distributions 487 33.1 Benini 487 33.2 Birnbaum–Saunders 488 33.3 Bradford 490 33.4 Champernowne 491 33.5 Davis 492 33.6 Fréchet 494 33.7 Gompertz 496 33.8 Shifted Gompertz 497 33.9 Gompertz–Makeham 498 33.10 Gamma-Gompertz 499 33.11 Hyperbolic 499 33.12 Asymmetric Laplace 502 33.13 Log-Laplace 504 33.14 Lindley 506 33.15 Lindley-Geometric 507 33.16 Generalised Lindley 509 33.17 Mielke 509 33.18 Muth 510 33.19 Nakagami 512 33.20 Power 513 33.21 Two-Sided Power 514 33.22 Exponential Power 516 33.23 Rician 517 33.24 Topp–Leone 517 33.25 Generalised Tukey Lambda 519 33.26 Wakeby 521 34. Amoroso Distribution 525 35. Binomial Distribution 529 35.1 Negative-Binomial 529 35.2 Pόlya 531 35.3 Geometric 531 35.4 Beta-Geometric 535 35.5 Yule–Simon 536 35.6 Beta-Binomial 538 35.7 Beta-Negative Binomial 540 35.8 Beta-Pascal 541 35.9 Gamma-Poisson 542 35.10 Conway–Maxwell–Poisson 543 35.11 Skellam 546 36. Other Discrete Distributions 549 36.1 Benford 549 36.2 Borel–Tanner 552 36.3 Consul 555 36.4 Delaporte 556 36.5 Flory–Schulz 558 36.6 Hypergeometric 559 36.7 Negative Hypergeometric 561 36.8 Logarithmic 561 36.9 Discrete Weibull 563 36.10 Zeta 564 36.11 Zipf 565 36.12 Parabolic Fractal 567 Appendix 1 Data Used in Examples 569 Appendix 2 Summary of Distributions 577 References 591 Index 593

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Book SynopsisDistillation Principles and Practice Second Edition covers all the main aspects of distillation including the thermodynamics of vapor/liquid equilibrium, the principles of distillation, the synthesis of distillation processes, the design of the equipment, and the control of process operation.Table of Contents1 Introduction 1 1.1 Principle of Distillation Separation 1 1.2 Historical 3 2 Vapor-Liquid Equilibrium 7 2.1 Basic Thermodynamic Correlations 7 2.1.1 Measures of Concentration 7 2.1.2 Equations of State (EOS) 8 2.1.3 Molar Mixing and Partial Molar State Variables 12 2.1.4 Saturation Vapor Pressure and Boiling Temperature of Pure Components 13 2.1.5 Fundamental Equation and the Chemical Potential 14 2.1.6 Gibbs-Duhem Equation and Gibbs-Helmholtz Equation 17 2.2 Calculation of Vapor-Liquid Equilibrium in Mixtures 18 2.2.1 Basic Equilibrium Conditions 18 2.2.2 Gibbs Phase Rule 19 2.2.3 Correlations for the Chemical Potential 19 2.2.4 Calculating Activity Coefficients with the Molar Excess Free Energy 23 2.2.5 Thermodynamic Consistency Check of Molar Excess Free Energy and Activity Coefficients 28 2.2.6 Iso-fugacity Condition 30 2.2.7 Fugacity of the Liquid Phase 30 2.2.8 Fugacity of the Vapor Phase 31 2.2.9 Vapor-Liquid Equilibrium Using an Equation of State 32 2.2.10 Fugacity of Pure Liquid as Standard Fugacity: Raoult’s Law 47 2.2.11 Fugacity of Infinitely Diluted Component as Standard Fugacity: Henry’s Law 48 2.2.12 Correlations describing the Molar Excess Free Energy and Activity Coefficients 49 2.2.13 Using Experimental Data of Binary Mixtures for Correlations Describing the Molar Excess Free Energy and Activity Coefficients .55 2.2.14 Vapor-Liquid Equilibrium Ratio of Mixtures 59 2.2.15 Relative Volatility of Mixtures 59 2.2.16 Boiling Condition of Liquid Mixtures 61 2.2.17 Condensation (Dew Point) Condition of Vapor Mixtures .62 2.3 Binary Mixtures and Phase Diagrams 81 2.3.1 Boiling Curve Correlation 81 2.3.2 Condensation (Dew Point) Curve Correlation 83 2.3.3 (p, x, y)-Diagram.84 2.3.4 (T, x, y)-Diagram 84 2.3.5 McCabe-Thiele Diagram 86 2.3.6 Boiling and Condensation Behavior of Binary Mixtures 86 2.3.7 General Aspects of Azeotropic Mixtures 90 2.3.8 Limiting Cases of Binary Mixtures 104 2.4 Ternary Mixtures 114 2.4.1 Boiling and Condensation Conditions of Ternary Mixtures 114 2.4.2 Triangular Diagrams 116 2.4.3 Boiling Surfaces 116 2.4.4 Condensation Surfaces 122 2.4.5 Derivation of Distillation Lines .123 2.4.6 Examples for Distillation Lines 128 3 Single Stage Distillation and Condensation 137 3.1 Continuous Closed Distillation and Condensation 137 3.1.1 Closed Distillation of Binary Mixtures 137 3.1.2 Closed Distillation of Multicomponent Mixtures 140 3.2 Batchwise Open Distillation and Open Condensation 152 3.2.1 Binary Mixtures .152 3.2.2 Ternary Mixtures 157 3.2.3 Multicomponent Mixtures 167 3.3 Semi-continuous Single Stage Distillation 169 3.3.1 Semi-continuous Single Stage Distillation of Binary Mixtures 169 4 Multistage Continuous Distillation (Rectification) 173 4.1 Principles 173 4.1.1 Equilibrium-Stage Concept 176 4.1.2 Transfer-Unit Concept 177 4.1.3 Comparison of Equilibrium-Stage and Transfer-Unit Concepts 180 4.2 Multistage Distillation of Binary Mixtures 181 4.2.1 Calculations Based on Material Balances 182 4.2.2 Calculation Based on Material and Enthalpy Balances 189 4.2.3 Distillation of Binary Mixtures at Total Reflux and Reboil .192 4.2.4 Distillation of Binary Mixtures at Minimum Reflux and Reboil 198 4.2.5 Energy Requirement for Distillation of Binary Mixtures.204 4.3 Multistage Distillation of Ternary Mixtures 206 4.3.1 Calculations Based on Material Balances 208 4.3.2 Distillation of Ternary Mixtures at Total Reflux and Reboil 215 4.3.3 Distillation of Ternary Mixtures at Minimum Reflux and Reboil 224 4.3.4 Energy Requirement of Ternary Distillation 248 4.4 Multistage Distillation of Multicomponent Mixtures 255 4.4.1 Rigorous Column Simulation 256 5 Reactive Distillation, Catalytic Distillation 283 5.1 Fundamentals 284 5.1.1 Chemical Equilibrium 284 5.1.2 Stoichiometric Lines 284 5.1.3 Non-Reactive and Reactive Distillation Lines .287 5.1.4 Reactive Azeotropes 289 5.2 Topology of Reactive Distillation Lines 293 5.2.1 Reactions in Ternary Systems 293 5.2.2 Reactions in Ternary Systems with Inert Components 295 5.2.3 Reactions with Side Products 297 5.2.4 Reactions in Quaternary Systems.298 5.3 Topology of Reactive Distillation Processes 298 5.3.1 Single Product Reactions 300 5.3.2 Decomposition Reactions.302 5.3.3 Side Reactions 306 5.4 Arrangement of Catalysts in Columns 307 5.4.1 Homogeneous Catalyst.307 5.4.2 Heterogeneous Catalyst 308 6 Multistage Batch Distillation 313 6.1 Batch Distillation of Binary Mixtures 314 6.1.1 Operation with Constant Reflux 315 6.1.2 Operation with Constant Distillate Composition 318 6.1.3 Operation with Minimum Energy Input 323 6.1.4 Comparison of Energy Requirement for Different Modes of Distillation.327 6.2 Batch Distillation of Ternary Mixtures 327 6.2.1 Zeotropic Mixtures 328 6.2.2 Azeotropic Mixtures 332 6.3 Batch Distillation of Multicomponent Mixtures 336 6.4 Influence of Column Liquid Hold-up on Batch Distillation 337 6.5 Processes for Separating Zeotropic Mixtures by Batch Distillation 340 6.6 Processes for Separating Azeotropic Mixtures by Batch Distillation 341 6.6.1 Processes in One Distillation Field 342 6.6.2 Processes in Two Distillation Fields 343 6.6.3 Process Simplifications 348 6.6.4 Hybrid Processes 348 7 Energy Economization in Distillation 357 7.1 Energy Requirement of Single Columns 358 7.1.1 Reduction of Energy Requirement 358 7.1.2 Reduction of Exergy Losses 359 7.2 Optimal Separation Sequences of Ternary Distillation 363 7.2.1 Process and Energy Requirement of the a-Path 363 7.2.2 Process and Energy Requirement of the c-Path.365 7.2.3 Process and Energy Requirement of the Preferred a/c-Path 366 7.3 Modifications of the Basic Processes 368 7.3.1 Material (Direct) Coupling of Columns.368 7.3.2 Processes with Side Columns 370 7.3.3 Thermal (Indirect) Coupling of Columns 386 7.4 Design of Heat Exchanger Networks 390 7.4.1 Optimum Heat Exchanger Networks 392 7.4.2 Modifying the Optimum Heat Exchanger Network 397 7.4.3 Dual Flow Heat Exchangers Networks 401 7.4.4 Process Modifications 401 8 Industrial Distillation Processes 407 8.1 Constraints for Industrial Distillation Processes 407 8.2 Fractionation of Binary Mixtures 412 8.2.1 Recycling of Diluted Sulfuric Acid 412 8.2.2 Ammonia Recovery from Waste Water 414 8.2.3 Hydrogen Chloride Recovery from Inert Gases .416 8.2.4 Linde Process for Air Separation 418 8.2.5 Process Water Purification 421 8.2.6 Steam Distillation 425 8.3 Fractionation of Multicomponent Zeotropic Mixtures 429 8.3.1 Separation Paths 429 8.3.2 Processes with Side Columns 431 8.4 Fractionation of Heterogeneous Azeotropic Mixtures 435 8.5 Fractionation of Azeotropic Mixtures by Pressure Swing Processes 436 8.6 Fractionation of Azeotropic Mixtures by Addition of an Entrainer 439 8.6.1 Processes for Systems without Distillation Boundary 440 8.6.2 Processes for Systems with Distillation Boundary 443 8.6.3 Hybrid Processes.455 8.7 Industrial Processes of Reactive Distillation 469 8.7.1 Synthesis of MTBE 469 8.7.2 Synthesis of Mono-Ethylene Glycol 471 8.7.3 Synthesis of TAME 473 8.7.4 Synthesis of Methyl-Acetate 474 9 Design of Mass Transfer Equipment 481 9.1 Types of Design 482 9.1.1 Tray Columns.482 9.1.2 Packed Columns 484 9.1.3 Criteria for Use of Tray or Packed Columns 486 9.2 Design of Tray Columns 487 9.2.1 Design Parameters of Tray Columns 487 9.2.2 Operating Region of Tray Columns 489 9.2.3 Two-Phase Flow on Trays 497 9.2.4 Mass Transfer in the Two-Phase Layer on Column Trays 518 9.3 Design of Packed Columns 533 9.3.1 Design Parameters of Packed Columns 534 9.3.2 Operating Region of Packed Columns 545 9.3.3 Two-Phase Flow in Packed Columns .548 9.3.4 Mass Transfer in Packed Columns 568 9.4 Appendix to Chapter 9: Pressure Drop in Packed Beds 587 10 Control of Distillation Processes 601 10.1 Control Loops 602 10.1.1 Single Control Loop 602 10.1.2 Ratio Control Loop 604 10.1.3 Disturbance Feed Forward Control Loop 604 10.1.4 Cascade Control Loop 605 10.2 Single Control Tasks for Distillation Columns 605 10.2.1 Liquid Level Control 605 10.2.2 Split Stream Control 606 10.2.3 Pressure Control 611 10.2.4 Product Concentration Control 613 10.3 Basic Control Configurations of Distillation Columns 613 10.3.1 Basic Control Systems without Composition Control 617 10.3.2 One-Point Composition Control Configurations 623 10.3.3 Two-Point Composition Control Configurations 626 10.4 Application Ranges of the Basic Control Configurations 629 10.4.1 Impact of Split Parameters according to Split Rule 2.629 10.4.2 Sharp Separations of Ideal Mixtures with Constant Relative Volatility at Minimum Reflux and Boilup Ratio 639 10.4.3 Extended Application Ranges of the Basic Control Configurations 643 10.5 Examples for Control Configurations of Distillation Processes 646 10.5.1 Azeotropic Distillation Process by Pressure Change.646 10.5.2 Distillation Process for Air Separation 647 10.5.3 Distillation Process with a Main and a Side Column 649 10.5.4 Azeotropic Distillation Process by Using an Entrainer 650 10.6 Control Configurations for Batch Distillation Processes 651 Index 655

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Book SynopsisA reference for engineers, scientists, and academics who want to be abreast of the latest industrial separation/treatment technique, this new volume aims at providing a holistic vision on the potential of advanced membrane processes for solving challenging separation problems in industrial applications. Separation processes are challenging steps in any process industry for isolation of products and recycling of reactants. Membrane technology has shown immense potential in separation of liquid and gaseous mixtures, effluent treatment, drinking water purification and solvent recovery. It has found endless popularity and wide acceptance for its small footprint, higher selectivity, scalability, energy saving capability and inherent ease of integration into other unit operations. There are many situations where the target component cannot be separated by distillation, liquid extraction, and evaporation. The different membrane processes such as pervaporation, vapor permeatioTable of ContentsPreface xvii 1 Tackling Challenging Industrial Separation Problems through Membrane Processes 1 Siddhartha Moulik, Sowmya Parakala and S. Sridhar 1.1 Water: The Source of Life 2 1.2 Significance of Water/Wastewater Treatment 5 1.3 Wastewater Treatment Techniques 8 1.4 Membrane Technologies for Water/Wastewater Treatment 11 1.5 Membranes: Materials, Classification and Configurations 12 1.5.1 Types of Membranes 12 1.5.1.1 Symmetric Membranes 12 1.5.1.2 Asymmetric Membranes 13 1.5.1.3 Electrically Charged Membranes 14 1.5.1.4 Inorganic Membranes 14 1.5.2 Membranes Modules and Their Characteristics 14 1.6 Introduction to Membrane Processes 17 1.6.1 Conventional Membrane Processes 17 1.7 CSIR-IICT’s Contribution for Water/Wastewater Treatment 21 1.7.1 Nanofiltration Plant for Processing Coke Oven Wastewater in Steel Industry 22 1.8 Potential of Pervaporation (PV), Vapor Permeation (VP), and Membrane Distillation (MD) in Wastewater Treatment 24 1.9 Conclusion 32 References 33 2 Pervaporation Membrane Separation: Fundamentals and Applications 37 Siddhartha Moulik, Bukke Vani, D. Vaishnavi and S. Sridhar 2.1 Introduction and Historical Perspective 38 2.2 Principle 40 2.2.1 Mass Transfer 42 2.2.2 Factors Affecting Membrane Performance 44 2.3 Membranes for Pervaporation 45 2.4 Applications of Pervaporation 46 2.4.1 Solvent Dehydration 46 2.4.2 Organophilic Separation 55 2.4.2.1 Removal of VOCs 57 2.4.2.2 Extraction of Aroma Compounds 58 2.4.3 Organic/Organic Separation 64 2.4.3.1 Separation of Polar/Non-Polar Mixture 64 2.4.3.2 Separation of Aromatic/Alicyclic Mixtures 70 2.4.3.3 Separation of Aromatic/Aliphatic/Aromatic Hydrocarbons 71 2.4.3.4 Separation of Isomers 72 2.5 Conclusions and Future Prospects 77 References 78 3 Pervaporation for Ethanol-Water Separation and Effect of Fermentation Inhibitors 89 Anjali Jain, Sushant Upadhyaya, Ajay K. Dalai and Satyendra P. Chaurasia 3.1 Introduction 90 3.2 Theory of Pervaporation 91 3.2.1 Applications of Pervaporation 92 3.2.2 Advantages of Pervaporation 93 3.2.3 Pervaporation Performance Evaluation Parameters 93 3.3 Various Membranes Used for the Recovery of Ethanol 94 3.3.1 Organic Membranes 94 3.3.2 Inorganic Membranes 102 3.3.3 Mixed Matrix Membranes 104 3.4 Effects of Process Variables on Ethanol Concentration in PV 106 3.4.1 Effect of Feed Flow Rate 106 3.4.2 Effect of Ethanol Concentration in Feed 107 3.4.3 Effect of Feed Temperature 108 3.4.4 Effect of Permeate Pressure 109 3.5 Effect of Fermentation Inhibitors on Pervaporation Performance 109 3.5.1 Effect of Furfural Concentration 112 3.5.2 Influence of Hydroxymethyl-Furfural 113 3.5.3 Effect of Vanillin 114 3.5.4 Effect of Acetic Acid 115 3.5.5 Effect of Catechol 116 3.6 Conclusions 116 References 117 4 Dehydration of Acetonitrile Solvent by Pervaporation through Graphene Oxide/Poly(Vinyl Alcohol) Mixed Matrix Membranes 123 Siddhartha Moulik, D.Vaishnavi and S.Sridhar 4.1 Introduction 124 4.2 Materials and Methods 126 4.2.1 Materials 126 4.2.2 Preparation of Graphene Oxide 126 4.2.3 Fabrication of GO Membrane 127 4.2.4 Structural Characterization of GO/PVA Mixed Matrix Membrane 127 4.2.5 Pervaporation Experiments 127 4.2.6 Determination of Diffusion Coefficients 129 4.2.7 Membrane Characterization 130 4.2.8 Hydrodynamic Simulation 130 4.2.8.1 Specification of Computational Domain and Governing Equations 130 4.3 Results and Discussions 132 4.3.1 Scanning Electron Microscope 132 4.3.2 Differential Scanning Calorimeter 132 4.3.3 Effect of GO concentration on PV Performance 134 4.3.4 Sorption Behavior 135 4.3.5 Concentration Distribution of Water within the Membrane 135 4.3.6 Effect of Feed Water Concentration 137 4.3.7 Effect of Permeate Pressure 137 4.4 Conclusions 139 References 139 5 Recovery of Acetic Acid from Vinegar Wastewater Using Pervaporation in a Pilot Plant 141 Haresh K Dave and Kaushik Nath 5.1 Introduction 142 5.2 Materials and Methods 144 5.2.1 Chemicals and Membranes 144 5.2.2 Preparation and Cross-Linking of Membrane 144 5.2.3 Equilibrium Sorption in PVA-PES Membrane 144 5.2.4 Permeation Experimental Study 145 5.2.5 Flux and Separation Factor 146 5.2.6 Permeability and Membrane Selectivity 147 5.2.7 Diffusion and Partition Coefficient 147 5.2.8 Thermogravimetric Analysis 148 5.2.9 FTIR Analysis 148 5.2.10 AFM and SEM Analysis 148 5.2.11 Mechanical Properties 149 5.3 Results and Discussion 149 5.3.1 Sorption in PVA-PES Membrane 149 5.3.2 Effect of Feed Composition on Flux and Separation Factor 151 5.3.3 Activation Energy and Heat of Sorption 152 5.3.4 Permeability, Permeance and Intrinsic Membrane Selectivity 153 5.3.5 Diffusion and Partition Coefficient 154 5.3.6 Thermogravimetric Analysis 156 5.3.7 Surface Chemistry by FTIR Analysis 156 5.3.8 Surface Topology by AFM Analysis 159 5.3.9 Surface Topology by SEM Analysis 161 5.3.10 Mechanical Properties of the Membrane 162 5.3.11 Reusability of the Membrane 163 5.4 Conclusion 164 Nomenclature 165 Acknowledgement 165 References 166 6 Thermodynamic Models for Prediction of Sorption Behavior in Pervaporation 169 Reddi Kamesh, Sumana Chenna and K. Yamuna Rani 6.1 Introduction 170 6.2 Thermodynamic Models for Sorption 172 6.2.1 Flory-Huggins Models 172 6.2.1.1 Models for Single Liquid Sorption in Polymer 172 6.2.1.2 Models for Binary Liquid Sorption in Polymer 175 6.2.2 UNIQUAC Model 180 6.2.2.1 Calculation of Binary Solvent-Solvent Interaction Parameters (τij & τji) 181 6.2.2.2 Calculation of Binary Polymer-Solvent Interaction Parameters (τim, τmi & τjm, τmj) 184 6.2.2.3 Prediction of Sorption Levels for a Ternary System Using UNIQUAC Model 185 6.2.3 UNIQUAC-HB Model 187 6.2.3.1 Calculation of Binary Solvent-Solvent Interaction Parameters (τʹij and τʹji ) 187 6.2.3.2 Calculation of Binary Solvent-Polymer Interaction Parameters 188 6.2.3.3 Prediction of Sorption Levels for a Ternary System 189 6.2.4 Modified NRTL Model 190 6.2.4.1 Calculation of Binary Solvent-Solvent Interaction Parameters (τ12 & τ21) 192 6.2.4.2 Calculation of Binary Polymer-Solvent Interaction Parameters (τiM & τMi) 192 6.2.4.3 Prediction of Sorption Behavior for a Ternary System – Method 1 193 6.2.4.4 Prediction of Sorption Behavior for a Ternary System – Method 2 194 6.3 Computational Procedure 196 6.4 Case Study 202 6.5 Summary and Conclusions 207 References 208 7 Molecular Dynamics Simulation for Prediction of Structure-Property Relationships of Pervaporation Membranes 211 Shaik Nazia, Siddhartha Moulik, Jega Jegatheesan, Suresh K. Bhargava and S. Sridhar 7.1 Introduction and Historical Perspective 212 7.2 Molecular Dynamics (MD) Simulations 213 7.3 Calculation of Interaction Parameters 214 7.4 Calculation of Permeation Properties 216 7.5 Free Volume Analysis 220 7.6 Conclusions 224 References 224 8 Vapor Permeation: Fundamentals, Principles and Applications 227 Siddhartha Moulik, Sowmya Parakala and S. Sridhar 8.1 Introduction and Historical Perspective 228 8.2 Principle 229 8.3 Mass Transfer Models in Vapor Permeation 231 8.4 Membranes for VP 233 8.4.1 Inorganic Membranes 233 8.4.2 Polymeric Membranes: 236 8.4.3 Mixed Matrix Membranes (MMMs) 239 8.5 Applications of Vapor Permeation 243 8.6 Conclusions and Future Trends 252 References 252 9 Vapor Permeation - A Thermodynamic Perspective 257 Sujay Chattopadhyay 9.1 Introduction 258 9.2 Parameters Influencing Vapor Permeation 259 9.3 Sorption in Polymeric Materials 262 9.3.1 Sorption of Pure Liquid or Vapors 263 9.3.2 Sorption of Binary Mixtures of Liquids and Vapors 264 9.4 Vapor Permeation in Polymeric Membranes 265 9.4.1 Vapor Permeation Through Rubbery Membranes 265 9.4.2 Vapor Permeation Through Glassy Membranes 265 9.4.3 Vapor Permeation Through Crystalline Polymers 267 9.5 Thermodynamics of Penetrant/Polymer Membrane 268 9.6 Non-Equilibrium Thermodynamics 271 9.7 Design of Vapor Permeation Membrane with High Selectivity 273 9.8 Membranes and Membrane Modules 276 9.9 Applications of Vapor Permeation 277 9.10 Conclusion 279 References 280 10 Vapor Permeation: Theory and Modelling Perspectives 283 Harsha Nagar, P. Anand and S. Sridhar 10.1 Introduction 284 10.2 Advantages of Vapor Permeation Process 287 10.3 Mass Transfer Mechanism in VP Process 287 10.4 Fundamentals of Vapor Permeation Modelling 288 10.4.1 Solution-Diffusion Mechanisms 289 10.4.2 Diffusion Modelling 290 10.4.2.1 Multi-Component Diffusion 292 10.4.3 Solubility Modelling 293 10.4.3.1 Equation of State Approach 293 10.4.3.2 Lattice Fluid-Based Models 294 10.5 Case Studies of VP Modelling 296 10.5.1 Modelling of a Multi-Component System for Vapor Permeation Process 296 10.5.2 Cost Effective Vapor Permeation Process for Isopropanol Dehydration 298 10.5.3 Vapor Permeation Modeling for Inorganic Shell and Tube Membranes. 299 10.6 Conclusion 301 References 302 11 Membrane Distillation: Historical Perspective and a Solution to Existing Issues of Membrane Technology 305 Siddhartha Moulik, Sowmya Parakala and S. Sridhar 11.1 Introduction and Historical Perspective of Membrane Distillation 306 11.2 Principle of Membrane Distillation 308 11.3 Mass Transfer in MD 312 11.4 Parameters Affecting Performance of MD 314 11.5 Heat Transfer in MD 317 11.6 Membranes for MD 318 11.7 Applications of Membrane Distillation 328 11.7.1 Seawater Desalination 328 11.7.2 Drinking Water Purification 333 11.7.3 Oily Wastewater Treatment 338 11.7.4 Solvent Dehydration 340 11.7.5 Treatment of Textile Industrial Effluent 343 11.7.6 Food Industrial Applications 345 11.7.7 Treatment of Radioactive Waste Water 346 11.7.8 Dairy Effluent Treatment 347 11.8 Conclusions and Future Trends 350 References 351 12 Dewatering of Diethylene Glycol and Lactic Acid Solvents by Membrane Distillation Technique 357 M. Madhumala, I. Ravi Kiran, Shakarachar M. Sutar and S. Sridhar 12.1 Introduction 358 12.2 Materials and Methods 360 12.2.1 Materials 360 12.2.2 Membrane Synthesis 360 12.2.2.1 Synthesis of Microporous Hydrophobic ZSM-5/PVC Mixed Matrix Membrane 360 12.2.2.2 Synthesis of Ultraporous Hydrophobic Polyvinylchloride Membrane 361 12.2.3 Experimental 361 12.2.3.1 Description of Membrane Distillation Set-up 361 12.2.3.2 Experimental Procedure 362 12.2.4 Membrane Characterization Techniques 363 12.2.4.1 Fourier Transform Infrared Spectroscopy (FT-IR) 363 12.2.4.2 X-Ray Diffraction Studies (XRD) 363 12.2.4.3 Thermo Gravimetric Analysis (TGA) 364 12.2.4.4 Scanning Electron Microscopy (SEM) 364 12.2.4.5 Contact Angle Measurement 364 12.3 Results and Discussion 364 12.3.1 Membrane Characterization 364 12.3.1.1 FTIR 364 12.3.1.2 XRD 366 12.3.1.3 TGA 367 12.3.1.4 SEM 368 12.3.1.5 Contact Angle Measurement 369 12.3.2 Case Study 1: Dehydration of Lactic Acid Using ZSM-5 Loaded Polyvinyl Chloride Membrane 369 12.3.2.1 Effect of Feed Lactic Acid Concentration on Membrane Performance 369 12.3.3 Case Study 2: Dehydration of Diethylene Glycol Using Ultraporous PVC Membrane 371 12.3.3.1 Effect of Feed Diethylene Glycol Concentration on Membrane Performance 371 12.4 Conclusions 372 References 373 13 Graphene Oxide/Polystyrene Mixed Matrix Membranes for Desalination of Seawater through Vacuum Membrane Distillation 375 Siddhartha Moulik, Sowmya Parakala and S. Sridhar 13.1 Introduction 376 13.1.1 Graphene and its Derivatives 378 13.2 Materials and Methods 380 13.2.1 Materials 380 13.2.2 Preparation of Graphene Oxide 380 13.2.3 Membrane Synthesis 381 13.2.4 Performance of the Crosslinked GO Loaded PS Membrane 382 13.2.5 Membrane Distillation Experiment 383 13.2.6 Membrane Characterization 384 13.2.7 Computational Fluid Dynamics Study 384 13.2.7.1 Model Development 384 13.3 Results and Discussions 388 13.3.1 Membrane Characterization 388 13.3.1.1 SEM 388 13.3.1.2 Contact Angle Measurement 389 13.3.1.3 FTIR 390 13.3.1.4 Raman Spectra 391 13.3.2 Effect of GO Concentration on MD Performance 391 13.3.3 Concentration Profile of Water Vapor within the Membrane 392 13.3.4 Effect of Feed Salt Concentration 393 13.3.5 Effect of Degree of Vacuum on MD Performance 395 13.3.6 Effect of Membrane Thickness 395 13.4 Conclusion 396 References 397 14 Vacuum Membrane Distillation for Water Desalination 399 Sushant Upadhyaya, Kailash Singh, S.P. Chaurasia, Rakesh Baghel and Sarita Kalla 14.1 Introduction 400 14.2 Membrane Distillation 400 14.2.1 Direct Contact Membrane Distillation (DCMD) 400 14.2.2 Air Gap Membrane Distillation (AGMD) 401 14.2.3 Sweeping Gas Membrane Distillation (SGMD) 401 14.2.4 Vacuum Membrane Distillation (VMD) 401 14.3 Selection Criteria for MD Membrane 402 14.4 Characterization of Membranes in MD 403 14.5 Applications 403 14.6 Modelling in MD 404 14.7 Mass and Heat Transport in VMD 407 14.8 Recovery Modelling in VMD 410 14.9 Operating Variables Influence on VMD Process 411 14.9.1 Variation in Permeate Flux with Feed Rate 411 14.9.2 Variation in Permeate Flux with Feed Inlet Temperature 412 14.9.3 Variation in Permeate Flux with Permeate Pressure 415 14.9.4 Variation in Permeate Flux with Feed Salt Concentration 416 14.9.5 Effect of Runtime 417 14.10 Water Recovery 418 14.11 Fouling on Membrane 420 14.12 Conclusions 424 Nomenclature 425 Greek Symbols 426 References 426 15 Glycerol Purification Using Membrane Technology 431 Priya Pal, S.P.Chaurasia, Sushant Upadhyaya, Madhu Agarwal and S. Sridhar 15.1 Introduction 432 15.2 Glycerol 433 15.2.1 Impurities Present in Crude Glycerol 433 15.3 Sources of Glycerol 434 15.3.1 Transesterification Reaction 435 15.3.2 Saponification of Oils and Fats 436 15.3.3 Hydrolysis of Oils and Fats 436 15.4 Purification Processes 440 15.4.1 Conventional Method (Physicochemical Method) 440 15.4.1.1 Pre-Treatment (Acidification and Neutralization) 440 15.4.1.2 Solvent Removal 441 15.4.1.3 Activated Charcoal Treatment for Color Removal 442 15.4.1.4 Ion-Exchange Adsorption 442 15.4.2 Membrane Technology 443 15.4.2.1 Membrane Distillation (MD) 443 15.4.2.2 Operating Variables Affecting VMD Process 447 15.5 Material and Methods 453 15.5.1 Materials 453 15.5.2 Synthesis of Hydrophobic Polyvinylidene Fluoride (PVDF) Membrane 453 15.5.3 Methods 453 15.5.4 Membrane Characterization 455 15.5.4.1 Scanning Electron Microscopy (SEM) 455 15.5.4.2 Membrane Porosity Measurement 455 15.5.4.3 Membrane Thickness 456 15.5.4.4 Contact Angle 456 15.5.4.5 FTIR 457 15.6 Results and Discussion 457 15.6.1 Characterization of Membrane 457 15.6.2 Effect of Glycerol Concentration on Flux and Percentage Rejection 459 15.7 Conclusions 459 Nomenclature 460 References 461 16 Reclamation of Water and Toluene from Bulk Drug Industrial Effluent by Vacuum Membrane Distillation 467 Pavani Vadthya, Y.V.L. Ravikumar and S. Sridhar 16.1 Introduction 468 16.2 Materials and Methods 469 16.2.1 Materials 469 16.2.2 Membrane Synthesis 469 16.2.3 Membrane Characterization 470 16.2.3.1 Fourier-Transform Infrared Spectroscopy (FTIR) 470 16.2.3.2 Scanning Electron Microscopy (SEM) 470 16.2.3.3 X-Ray Diffraction Studies (XRD) 470 16.2.3.4 Sorption Studies 470 16.2.4 Experimental Set Up 471 16.2.5 Experimental Procedure 471 16.2.6 Flux 471 16.2.7 Refractive Index 472 16.3 Results and Discussion 472 16.3.1 Membrane Characterization 472 16.3.1.1 FTIR 472 16.3.1.2 SEM 473 16.3.1.3 XRD 473 16.3.1.4 Sorption Studies 474 16.3.2 Effect of Membrane Thickness 476 16.3.3 Effect of Polymer Loading 476 16.3.4 Effect of Permeate Pressure 477 16.4 Conclusions 479 References 480 Index 481

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Book Synopsis Comprehensive and consolidated resource covering the evaluation of imidazoline inhibitors for safeguarding pipeline steels against corrosion, with supporting case studies Imidazoline Inhibitors for Corrosion Protection of Oil Pipeline Steels represents a comprehensive compilation of the experimental findings that delve into the evaluation of imidazoline inhibitors for safeguarding pipeline steels against corrosion, consolidating invaluable insights and discoveries from a multitude of investigations. The experimental methodologies employed encompass a diverse range of techniques, enabling a thorough exploration of the inhibitive properties of imidazoline compounds. The book explores the significance of various corrosion control strategies, including the utilization of a variety of inhibitors, the implementation of pigging techniques, the application of cathodic protection, and the relevant codes and standards. To aid in reader comprehension, the book presents a

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Book SynopsisWritten by two of the most prolific and respected chemical engineers in the world, this groundbreaking two-volume set is the new standard in the industry, offering engineers and students alike the most up-do-date, comprehensive, and state-of-the-art coverage of processes and best practices in the field today. This first new volume in a two-volume set explores and describes integrating new tools for engineering education and practice for better utilization of the existing knowledge on process design. Useful not only for students, professors, scientists and practitioners, especially process, chemical, mechanical and metallurgical engineers, it is also a valuable reference for other engineers, consultants, technicians and scientists concerned about various aspects of industrial design. The text can be considered as a complementary text to process design for senior and graduate students as well as a hands-on reference work or refresher for engineers at entry level.Table of ContentsPreface xvii Acknowledgments xix About the Authors xxi 1 Computations with Excel Spreadsheet-UniSim Design Simulation 1 Section I - Numerical Analysis 1 Introduction 1 Excel Spreadsheet 1 Functions 2 Trendline Coefficients 2 Goal Seek 5 Solver 6 Linear Regression 7 Measuring Regression Quality 9 Multiple Regression 9 Polynomial Regression 11 Simultaneous Linear Equations 11 Nonlinear Equations 12 Interpolations 13 Integrations 14 The Trapezoidal Rule 14 Simpson’s 1/3 Rule 15 Simpson’s 3/8 Rule 15 Differential Equations 15 Nth Order Ordinary Differential Equations 15 Solution of First-Order Ordinary Differential Equations 15 Runge-Kutta Methods 16 Examples and Solutions 17 Section II – Process Simulation 28 Introduction 28 Thermodynamics for Process Simulators 29 UNISIM Design Software 30 Examples and Solutions 31 References 78 2 Physical Property of Pure Components and Mixtures 81 Pure Components 81 Density of Liquid 82 Viscosity of Liquid 83 Heat Capacity of Liquid 85 Thermal Conductivity of Liquid 87 Volumetric Expansion Rate 90 Vapor Pressure 91 Viscosity of Gas 93 Thermal Conductivity of Gas 94 Heat Capacity of Gases 95 Mixtures 97 Surface Tensions 98 Viscosity of Gas Mixture 99 Enthalpy of Formation 101 Enthalpy of Vaporization 103 Gibbs Energy of Reaction 105 Henry’s Law Constant for Gases in Water 107 Coefficient of Thermal Expansion of Liquid 108 Diffusion Coefficients 109 Gas-Phase Diffusion Coefficients 109 Liquid-Phase Diffusion Coefficients 110 Compressibility Z-factor 111 Solubility and Adsorption 116 Solubility of Hydrocarbons in Water 116 Solubility of Gases in Water 117 Solubility of Sulfur and Nitrogen Compounds in Water 118 Adsorption on Activated Carbon 119 References 119 3 Fluid Flow 121 Introduction 121 Flow of Fluids in Pipes 121 Equivalent Length of Various Fittings and Valves 123 Excess Head Loss 123 Pipe Reduction and Enlargement 124 Pressure Drop Calculations for Single-phase Incompressible Fluids 124 Friction Factor 127 Overall Pressure Drop 128 Nomenclature 130 Compressible Fluid Flow in Pipes 130 Maximum Flow and Pressure Drop 131 Critical or Sonic Flow and the Mach Number 131 Mach Number 132 Mathematical Model of Compressible Isothermal Flow 134 Flow Rate Through Pipeline 136 Pipeline Pressure Drop 138 Nomenclature 139 Subscripts 139 Two-phase Flow in Process Piping 139 Flow Patterns 140 Flow Regimes 142 Pressure Drop 142 Erosion-Corrosion 145 Nomenclature 145 Vapor-liquid Two-phase Vertical Downflow 146 The Equations 147 The Algorithm 147 Nomenclature 147 Line Sizes for Flashing Steam Condensate 148 The Equations 148 Nomenclature 149 Flow Through Packed Beds 150 The Equations 151 Nomenclature 152 Examples and Solutions 152 References 162 4 Equipment Sizing 165 Introduction 165 Sizing of Vertical and Horizontal Separators 166 Vertical Separators 166 Calculation Method for a Vertical Drum 168 Calculation Method for a Horizontal Drum 170 Liquid Holdup and Vapor Space Disengagement 171 Wire Mesh Pad 171 Standards for Horizontal Separators 172 Piping Requirements 172 Nomenclature 172 Sizing of Partly Filled Vessels and Tanks 173 The Equations 173 Nomenclature 175 Preliminary Vessel Design 176 Nomenclature 177 Cyclone Design 178 Introduction 178 Cyclone Design Procedure 178 The Equations 179 Saltation Velocity 180 Pressure Drop 181 Troubleshooting Cyclone Maloperations 182 Cyclone Collection Efficiency 182 Cyclone Design Factor 182 Cyclone Design Procedure 183 Nomenclature 183 Gas Dryer Design 184 The Equations 186 Pressure Drop 187 Desiccant Reactivation 188 Nomenclature 188 Examples and Solutions 189 References 194 5 Instrument Sizing 195 Introduction 195 Variable-Head Meters 195 Macroscopic Mechanical Energy Balance 196 Variable-Head Meters 196 Orifice Sizing for Liquid and Gas Flows 200 Orifice Sizing for Liquid Flows 201 Orifice Sizing for Gas Flows 202 Orifice Sizing for Liquid Flow 204 Orifice Sizing for Gas Flow 204 Types of Restriction Orifice Plates 205 Case Study 1 205 Nomenclature 212 Control Valve Sizing 221 Introduction 221 Control Valve Characteristics 223 Pressure Drop for Sizing 224 Choked Flow 224 Flashing and Cavitation 224 Control Valve Sizing for Liquid, Gas, Steam and Two-Phase Flows 225 Liquid Sizing 226 Gas Sizing 227 Critical Condition 227 Steam Sizing 227 Two-Phase Flow 228 Installation 229 Noise 229 Control Valve Sizing Criteria 230 Valve Sizing Criteria 230 Self-Acting Regulators 231 Types of Self-Acting Regulators 231 Case Study 2 233 Rules of Thumb 246 Nomenclature 246 References 247 6 Pumps and Compressors Sizing 249 Pumps 249 Introduction 249 Pumping of Liquids 249 Pump Design Standardization 252 Basic Parts of a Centrifugal Pump 253 Impellers 253 Casing 253 Shaft 254 Centrifugal Pump Selection 255 Single-Stage (Single Impeller) Pumps 256 Hydraulic Characteristics for Centrifugal Pumps 260 Friction Losses Due to Flow 269 Velocity Head 269 Friction 271 Net Positive Suction Head (npsh) and Pump Suction 271 General Suction System 277 Reductions in NPSHR 279 Corrections to NPSHR for Hot Liquid Hydrocarbons and Water 279 Charting NPSHR Values of Pumps 280 Net Positive Suction Head (NPSH) 280 Specific Speed 282 “Type Specific Speed” 285 Rotative Speed 286 Pumping Systems and Performance 286 System Head Using Two Different Pipe Sizes in Same Line 288 Power Requirements for Pumping Through Process Lines 291 Hydraulic Power 292 Relations Between Head, Horsepower, Capacity, Speed 293 Brake Horsepower (BHP) Input at Pump 293 Affinity Laws 296 Pump Parameters 298 Specific Speed, Flowrate and Power Required by a Pump 299 Pump Sizing of Gas-Oil 301 Debutanizer Unit 303 Centrifugal Pump Efficiency 306 Centrifugal Pump Specifications 311 Pump Specifications 311 Steps in Pump Sizing 312 Reciprocating Pumps 313 Significant Features in Reciprocating Pump Arrangements 314 Application 316 Performance 316 Discharge Flow Patterns 317 Horsepower 318 Pump Selection 318 Selection Rules-of-Thumb 318 A Case Study 321 Pump Simulation on a PFD 321 Variables Descriptions 322 Simulation Algorithm 322 Problem 323 Discussion 324 Pump Cavitation 332 Factors in Pump Selection 333 Compressors 334 Introduction 334 General Application Guide 334 Specification Guides 337 General Considerations for Any Type of Compressor Flow Conditions 337 Fluid Properties 338 Compressibility 338 Corrosive Nature 338 Moisture 339 Special Conditions 339 Specification Sheet 339 Performance Considerations 339 Cooling Water to Cylinder Jackets 339 Heat Rejected to Water 339 Drivers 340 Ideal Pressure – Volume Relationship 341 Actual Compressor Diagram 343 Deviations From Ideal Gas Laws: Compressibility 343 Adiabatic Calculations 346 Charles’ Law at Constant Pressure 346 Amonton’s Law at Constant Volume 346 Combined Boyle’s and Charles’ Laws 346 Entropy Balance Method 347 Isentropic Exponent Method 347 Compression Ratio 354 Horsepower 356 Single Stage 356 Theoretical Hp 356 Actual Brake Horsepower, Bhp 356 Actual Brake Horsepower, Bhp (Alternate Correction for Compressibility) 361 Temperature Rise – Adiabatic 363 Temperature Rise – Polytropic 365 A Case Study Using Unisim Design R460.1 Software for a Two–stage Compression 365 Case Study 2 365 Solution 365 1. Starting UniSim Design Software 366 2. Creating a New Simulation 366 Saving the Simulation 367 3. Adding Components to the Simulation 367 4. Selecting a Fluids Package 368 5. Select the Units for the Simulation 369 6. Enter Simulation Environment 369 Accidentally Closing the PFD 371 Object Palette 371 7. Adding Material Streams 371 8. Specifying Material Streams 372 9. Adding A Compressor 374 Specifications 381 Compression Process 385 Adiabatic 385 Isothermal 385 Polytropic 385 Efficiency 388 Head 390 Adiabatic Head Developed Per Single-stage Wheel 390 Polytropic Head 391 Polytropic 391 Brake Horsepower 393 Speed of Rotation 396 Temperature Rise During Compression 397 Sonic or Acoustic Velocity 399 Mach Number 402 Specific Speed 402 Compressor Equations in Si Units 403 Polytropic Compressor 405 Adiabatic Compressor 408 Efficiency 409 Mass Flow Rate, w 409 Mechanical Losses 410 Estimating Compressor Horsepower 411 Multistage Compressors 412 Multicomponent Gas Streams 414 Affinity Laws 422 Speed 423 Impeller Diameters (Similar) 423 Impeller Diameter (Changed) 424 Effect of Temperature 424 Affinity Law Performance 425 Troubleshooting of Centrifugal and Reciprocating Compressors 425 Nomenclature 429 Greek Symbols 431 Subscripts 432 Nomenclature 432 Subscripts 434 Greek Symbols 434 References 434 Pumps 434 Bibliography 435 References 435 Compressors 435 Bibliography 436 7 Mass Transfer 437 Introduction 437 Vapor Liquid Equilibrium 437 Bubble Point Calculation 441 Dew Point Calculation 442 Equilibrium Flash Composition 442 Fundamental 443 The Equations 444 The Algorithm 445 Nomenclature 446 Tower Sizing for Valve Trays 446 Introduction 446 The Equations 448 Nomenclature 452 Greek Letters 465 Packed Tower Design 466 Introduction 466 Pressure Drop 466 Flooding 466 Operating and Design Conditions 468 Design Equations 471 Packed Towers versus Trayed Towers 473 Economic Trade-Offs 473 Nomenclature 474 Greek Letters 474 Determination of Plates in Fractionating Columns By the Smoker Equations 474 Introduction 474 The Equations 474 Application to a Distillation Column 475 Rectifying Section: 475 Stripping Section: 476 Nomenclature 476 Multicomponent Distribution and Minimum Trays In Distillation Columns 477 Introduction 477 Key Components 477 Equations Surveyed 477 Fractionating Tray Stability Diagrams 479 Areas of Unacceptable Operation 479 Foaming 480 Flooding 480 Entrainment 480 Weeping/Dumping 480 Fractionation Problem Solving Considerations 481 Mathematical Modeling 481 The Fenske’s Method for Total Reflux 483 The Gilliland Method for Number of Equilibrium Stages 484 The Underwood Method 485 Equations for Describing Gilliland’s Graph 486 Kirkbride’s Feed Plate Location 487 Nomenclature 487 Greek Letters 488 Examples and Solutions 488 References 499 Index 501

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Book SynopsisThis book provides a comprehensive treatment of investing chemical processing incidents. It presents on-the-job information, techniques, and examples that support successful investigations. Issues related to identification and classification of incidents (including near misses), notifications and initial response, assignment of an investigation team, preservation and control of an incident scene, collecting and documenting evidence, interviewing witnesses, determining what happened, identifying root causes, developing recommendations, effectively implementing recommendation, communicating investigation findings, and improving the investigation process are addressed in the third edition. While the focus of the book is investigating process safety incidents the methodologies, tools, and techniques described can also be applied when investigating other types of events such as reliability, quality, occupational health, and safety incidents.Table of ContentsPreface xxv Acknowledgments xxvii Acronyms and Abbreviations xxix 1 Introduction 1 1.1 Building on the Past 1 1.2 Investigation Basics 2 1.2.1 The First Step 2 1.2.2 The Second Step 4 1.2.3 The Third Step 4 1.2.4 The Fourth step 4 1.2.5 The Fifth Step 5 1.2.6 The Sixth Step 5 1.3 Who Should Read This Book? 5 1.4 The Guideline’s Objectives 6 1.5 The Guideline’s Content and Organization 6 1.6 The Continuing Evolution of Incident Investigation 11 2 Overview of Chemical Process Incident Causation 13 2.1 Stages of a Process-Related Incident 14 2.1.1 Three Phase Model of Process-Related Incidents 14 2.1.2 Event Tree 14 2.1.3 Swiss Cheese Model 16 2.1.4 Importance of Latent Failures 17 2.2 Key Causation Concepts 18 2.2.1 Loss of Containment or Energy 18 2.2.2 Management System Failure 20 2.2.3 Human Factors 21 2.2.4 Multiple Causation 22 2.2.5 Events vs Root Causes 22 2.2.6 Controlling Risk 23 2.3 Summary 24 3 An Overview of Investigation Methodologies 26 3.1 History of Investigation Methodologies and Tools 29 3.1.1 One-on-One Interview 29 3.1.2 Brainstorming 29 3.1.3 What If Analysis 30 3.1.4 5-Whys 30 3.1.5 Process of Elimination 31 3.1.6 Timelines 31 3.1.7 Sequence Diagrams 31 3.1.8 Predefined Trees 33 3.2 Tools for Use in Preparation for Root Cause Analysis 34 3.2.1 Timelines 34 3.2.2 Sequence Diagrams 35 3.2.3 Scientific Method 35 3.2.4 Causal Factor Identification 36 3.3 Structured Root Cause Analysis Methodologies 37 3.3.1 Checklists 37 3.3.2 Predefined Trees 38 3.3.3 Team-Developed Logic Trees 39 3.4 Selecting an Appropriate Methodology 43 3.4.1 Methodologies Used by CCPS Members 46 4 Designing An Incident Investigation Management System 47 4.1 System Considerations 49 4.1.1 An Organization’s Responsibilities 49 4.1.2 Workforce Responsibilities 51 4.1.3 Role of the Management System Developers 53 4.1.4 Integration with Other Functions and Teams 54 4.1.5 Involvement by Regulatory Agencies 55 4.2 Typical Management System Topics 58 4.2.1 Classifying Incidents 58 4.2.2 Specifying and Managing Documentation 59 4.2.3 Legal Considerations 60 4.2.4 Describing Team Organization and Functions 63 4.2.5 Electronic Process Data and Control Systems 64 4.2.6 Defining Training Requirements 65 4.2.7 Emphasizing Root Causes 69 4.2.8 Fostering a Blame-Free Policy 70 4.2.9 Developing Recommendations 70 4.2.10 Recommendation Responsibilities 71 4.2.11 Implementing the Recommendations and Follow-up Activities 72 4.2.12 Providing a Template for Formal Reports 73 4.2.13 Management System Review and Approval 73 4.2.14 Planning for Continuous Improvement 73 4.3 Management System 74 4.3.1 Initial Implementation— Training 75 4.3.2 Developing a Specific Investigation Plan 75 5 Initial Notification, Classification and Investigation of Process Safety Incidents 79 5.1 Internal Reporting 79 5.2 Incident Classification 81 5.2.1 Severity Classification 82 5.2.2 Local Jurisdiction 89 5.2.3 Other Options for Establishing Classification Criteria 89 5.3 Incident Notification 90 5.3.1 Corporate Notification 90 5.3.2 Agency Notification 91 5.3.3 Other Stakeholder Notification 91 5.3.4 Other Notifications 92 5.4 Type of Investigation 92 5.4.1 Which Investigation System to Use? 92 5.4.2 Investigation Approach 93 5.5 Summary 94 6 Building and Leading An Incident Investigation Team 96 6.1 Team Approach 96 6.2 Advantages of the Team Approach 97 6.3 Leading a Process Safety Incident Investigation Team 98 6.4 Potential Team Composition 100 6.5 Building a Team for a Specific Incident 104 6.5.1 Composition and Size of Investigation Team 104 6.6 Team Activities 106 6.7 Summary 108 7 Witness Management 110 7.1 Overview 110 7.1.1 Witness Issues Following a Major Occurrence 111 7.1.2 Investigation Team Priorities for Managing Witnesses 112 7.2 Identifying Witnesses 113 7.3 Witness Interviews 115 7.3.1 Human Factors Related to Interviews 115 7.3.2 Collecting Information from Witnesses 118 7.3.3 Initial Witness Statements 120 7.3.4 Conducting the Interview 121 7.4 Conducting Follow-up Activities 134 7.5 Conducting Follow-up Interviews 135 7.6 Reliability of Witness Statements 135 7.7 Summary 135 8 Evidence Identification, Collection and Management 137 8.1 Overview 137 8.1.1 Developing a Specific Plan 138 8.1.2 Investigation Environment Following a Major Occurrence 139 8.1.3 Priorities for Managing an Incident Investigation Team 141 8.2 Sources of Evidence 144 8.2.1 Types of Sources 144 8.2.2 Physical Evidence and Data 147 8.2.3 Paper Evidence and Data 149 8.2.4 Electronic Evidence and Data 152 8.2.5 Position Evidence and Data 153 8.3 Evidence Gathering 156 8.3.1 Initial Site Visit 157 8.3.2 Identifying and Documenting Evidence 159 8.3.3 Tools and Supplies 162 8.3.4 Photography and Video 164 8.4 Timelines and Sequence Diagrams 168 8.4.1 Constructing a Timeline 168 8.4.2 Constructing a Sequence Diagram 174 8.5 Summary 176 9 Evidence Analysis and Causal Factor Determination 178 9.1 Scientific Method 178 9.2 Confirmation Bias 181 9.3 Evidence Analysis 181 9.3.1 Data Organization - Timelines 182 9.3.2 Use of Protocols 182 9.3.3 Mechanical Failure Analysis 184 9.3.4 Advanced Data Systems 187 9.4 Hypothesis Formulation 187 9.4.1 Fact/Hypothesis Matrix 188 9.5 Hypothesis Testing 190 9.5.1 Engineering Analysis 190 9.5.2 Computational Modeling 191 9.5.3 Reconstruction 191 9.5.4 Test the Items under Simulated Conditions 192 9.5.5 Testing of Human Input/Performance 192 9.6 Select the Final Hypothesis 193 9.6.1 Causal Factor Identification 193 9.6.2 Causal Factor Charting 198 9.6.3 Developing a Causal Factor Chart 200 9.7 Summary 202 10 Determining Root Causes—Structured Approaches 203 10.1 Concept of Root Cause Analysis 203 10.2 Case Histories 206 10.3 Methodologies for Root Cause Analysis 208 10.3.1 5 Whys Technique 208 10.3.2 Structured Root Cause Determination 212 10.4 Root Cause Determination Using Logic Trees 214 10.4.1 Gather Evidence and List Facts 215 10.4.2 Timeline Development 215 10.4.3 Logic Tree Development 215 10.5 Building a Logic Tree 219 10.5.1 Choosing the Top Event 220 10.5.2 Logic Tree Basics 220 10.5.3 Example—Chemical Spray Injury 228 10.5.4 What to Do if the Process Stalls 232 10.5.5 Guidelines for Stopping Tree Development 232 10.6 Example Applications 235 10.6.1 Fire and Explosion Incident—Fault Tree 235 10.6.2 Data-Driven Cause Analysis 239 10.6.3 Logic Tree Summary 241 10.7 Root Cause Determination Using Predefined Trees 242 10.7.1 Scenario Determination 244 10.7.2 Causal Factors 244 10.7.3 Predefined Tree 245 10.8 Using Predefined Trees 246 10.8.1 Predefined Tree Methodology 247 10.8.2 Example—Environmental Incident 248 10.8.2 Quality Assurance 255 10.8.3 Predefined Tree Summary 255 10.9 Checklists 256 10.9.1 Use of Checklists 257 10.9.2 Checklist Summary 258 10.10 Human Factors Applications 258 10.11 Summary 259 11 The Impact of Human Factors 261 11.1 Human Factors Concepts 262 11.2 Incorporating Human Factors into the Incident Investigation Process 267 11.2.1 Human Factors Before and During the Incident 268 11.2.2 Human Factors during the Causal Analysis 269 11.2.3 Human Factors in Developing Recommendations 275 11.2.4 After the Investigation 275 11.3 Other References 276 11.4 Summary 276 12 Developing Effective Recommendations 278 12.1 Key Concepts 278 12.2 Developing Effective Recommendations 280 12.2.1 Team Responsibilities 280 12.2.2 Attributes of Good Recommendations 280 12.3 Types of Recommendations 283 12.3.1 Inherently Safer Design 284 12.3.2 Layers of Protection 285 12.3.3 Commendation/Disciplinary Action 289 12.3.4 The “Further Action Required” Recommendation 289 12.4 The Recommendation Process 290 12.4.1 Select Each Cause 290 12.4.2 Perform a Completeness Test 290 12.4.3 Assessing the Effectiveness 291 12.4.4 Prepare to Present Recommendations 291 12.4.5 Review Recommendations with Management 293 12.4.6 Tracking and Closure of Recommendations 293 12.5 Summary 294 13 Preparing the Final Report 295 13.1 Report Scope 295 13.2 Interim Reports 296 13.3 Writing the Report 297 13.4 Sample Report Format 299 13.4.1 Executive Summary 300 13.4.2 Introduction 301 13.4.3 Background 301 13.4.4 Sequence of Events and Description of the Incident 302 13.4.5 Findings 302 13.4.6 Causal Factors 303 13.4.7 Root Causes 304 13.4.8 Recommendations 304 13.4.9 Noncontributory Factors 306 13.4.10 Attachments or Appendices 306 13.5 Report Review and Quality Assurance 307 13.5.1 Reviewing the Report 307 13.5.2 Avoiding Common Mistakes 308 13.6 Investigation Document and Evidence Retention 310 13.7 Summary 311 14 Implementing Recommendations 314 14.1 Activities Related to Recommendation Implementation 315 14.2 Validation of Effectiveness – Case Studies 317 14.2.1 Nuclear Plant Incident 317 14.2.2 Aircraft Incident 318 14.2.3 Petrochemical Plant Incident 318 14.2.4 Challenger Space Shuttle Incident 318 14.2.5 Typical Plant Incidents 319 14.3 Practical Suggestions for Successful Recommendation Implementation 319 14.3.1 Assigning a Responsible Individual 320 14.3.2 Due Dates and Priorities to Implement Recommendations 320 14.3.3 Challenges to Resolving Recommendations 321 14.3.4 Tracking Action Items 323 14.3.5 Follow-up Verification 323 15 Continuous Improvement for the Incident Investigation System 326 15.1 Regulatory Compliance Review 327 15.2 Investigation Quality Assessment 329 15.3 Causal Category Analysis 331 15.4 Review of Near-Miss Events 334 15.5 Recommendations Review 334 15.6 Investigation Follow-up Review 336 15.7 Key Performance Indicators 337 15.8 Summary 338 16 Lessons Learned 340 16.1 Various Sources of Learning from Incidents 341 16.1.1 Internal Sources 341 16.1.2 External Sources 341 16.1.3 Cross-Industry 343 16.2 Identifying Learning Opportunities 343 16.3 Sharing and Institutionalizing Lessons Learned 345 16.4 Senior Management – Incident Sharing and Commitment 347 16.5 Examples of Sharing Lessons Learned 348 16.5.1 Creating a Process Safety Alert from a Case Study 348 16.5.2 Safety Newsletter 350 16.5.3 Videos of Incidents 355 16.5.4 Detailed Incident Reports and Databases 355 16.6 Summary 355 Appendix A. Photography Guidelines for Maximum Results 357 Appendix B. Example Protocol – Checking Position of a Chain Valve 362 Appendix C. Process Safety Events Leveling Criteria 366 Appendix D. Example Case Study 368 Appendix E. Quick Checklist for Investigators 398 Appendix F. Evidence Preservation Checklist – Prior to Arrival of the Investigation Team 404 Appendix G. Guidance On Classifying Potential Severity of a Loss of Primary Containment 406 Glossary 416 References 427 Index 437

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Book SynopsisA systematic approach to profit optimization utilizing strategic solutions and methodologies for the chemical process industry In the ongoing battle to reduce the cost of production and increase profit margin within the chemical process industry, leaders are searching for new ways to deploy profit optimization strategies. Profit Maximization Techniques For Operating Chemical Plants defines strategic planning and implementation techniques for managers, senior executives, and technical service consultants to help increase profit margins. The book provides in-depth insight and practical tools to help readers find new and unique opportunities to implement profit optimization strategies. From identifying where the large profit improvement projects are to increasing plant capacity and pushing plant operations towards multiple constraints while maintaining continuous improvementsthere is a plethora of information to help keep plant operations on budget. The book also includes informatiTable of ContentsFigure List xix Table List xxv Preface xxvii 1 Concept of Profit Maximization 1 1.1 Introduction 1 1.2 Who is This Book Written for? 3 1.3 What is Profit Maximization and Sweating of Assets All About? 4 1.4 Need for Profit Maximization in Today’s Competitive Market 7 1.5 Data Rich but Information Poor Status of Today’s Process Industries 8 1.6 Emergence of Knowledge-Based Industries 9 1.7 How Knowledge and Data Can Be Used to Maximize Profit 9 References 10 2 Big Picture of the Modern Chemical Industry 11 2.1 New Era of the Chemical Industry 11 2.2 Transition from a Conventional to an Intelligent Chemical Industry 11 2.3 How Will Digital Affect the Chemical Industry and Where Can the Biggest Impact Be Expected? 12 2.3.1 Attaining a New Level of Functional Excellence 12 2.3.1.1 Manufacturing 13 2.3.1.2 Supply Chain 14 2.3.1.3 Sales and Marketing 14 2.3.1.4 Research and Development 15 2.4 Using Advanced Analytics to Boost Productivity and Profitability in Chemical Manufacturing 15 2.4.1 Decreasing Downtime Through Analytics 16 2.4.2 Increase Profits with Less Resources 17 2.4.3 Optimizing the Whole Production Process 18 2.5 Achieving Business Impact with Data 19 2.5.1 Data’s Exponential Growing Importance in Value Creation 19 2.5.2 Different Links in the Value Chain 20 2.5.2.1 The Insights Value Chain – Definitions and Considerations 21 2.6 From Dull Data to Critical Business Insights: The Upstream Processes 22 2.6.1 Generating and Collecting Relevant Data 22 2.6.2 Data Refinement is a Two-Step Iteration 23 2.7 From Valuable Data Analytics Results to Achieving Business Impact: The Downstream Activities 25 2.7.1 Turning Insights into Action 25 2.7.2 Developing Data Culture 25 2.7.3 Mastering Tasks Concerning Technology and Infrastructure as Well as Organization and Governance 25 References 26 3 Profit Maximization Project (PMP) Implementation Steps 27 3.1 Implementing a Profit Maximization Project (PMP) 27 3.1.1 Step 1: Mapping the Whole Plant in Monetary Terms 27 3.1.2 Step 2: Assessment of Current Plant Conditions 27 3.1.3 Step 3: Assessment of the Base Control Layer of the Plant 28 3.1.4 Step 4: Assessment of Loss from the Plant 29 3.1.5 Step 5: Identification of Improvement Opportunity in Plant and Functional Design of PMP Applications 29 3.1.6 Step 6: Develop an Advance Process Monitoring Framework by Applying the Latest Data Analytics Tools 30 3.1.7 Step 7: Develop a Real-Time Fault Diagnosis System 30 3.1.8 Step 8: Perform a Maximum Capacity Test Run 30 3.1.9 Step 9: Develop and Implement Real-Time APC 31 3.1.10 Step 10: Develop a Data-Driven Offline Process Model for Critical Process Equipment 31 3.1.11 Step 11: Optimizing Process Operation with a Developed Model 32 3.1.12 Step 12: Modeling and Optimization of Industrial Reactors 32 3.1.13 Step 13: Maximize Throughput of All Running Distillation Columns 33 3.1.14 Step 14: Apply New Design Methodology for Process Equipment 33 References 34 4 Strategy for Profit Maximization 35 4.1 Introduction 35 4.2 How is Operating Profit Defined in CPI? 36 4.3 Different Ways to Maximize Operating Profit 36 4.4 Process Cost Intensity 37 4.4.1 Definition of Process Cost Intensity 37 4.4.2 Concept of Cost Equivalent (CE) 39 4.4.3 Cost Intensity for a Total Site 39 4.5 Mapping the Whole Process in Monetary Terms and Gain Insights 40 4.6 Case Study of a Glycol Plant 40 4.7 Steps to Map the Whole Plant in Monetary Terms and Gain Insights 43 4.7.1 Step 1: Visualize the Plant as a Black Box 43 4.7.2 Step 2: Data Collection from a Data Historian and Preparation of Cost Data 46 4.7.3 Step 3: Calculation of Profit Margin 46 4.7.4 Step 4: Gain Insights from Plant Cost and Profit Data 48 4.7.5 Step 5: Generation of Production Cost and a Profit Margin Table for One Full Year 51 4.7.6 Step 6: Plot Production Cost and Profit Margin for One Full Year and Gain Insights 51 4.7.7 Step 7: Calculation of Relative Standard Deviations of each Parameter in order to Understand the Cause of Variability 52 4.7.8 Step 8: Cost Benchmarking 53 Reference 54 5 Key Performance Indicators and Targets 55 5.1 Introduction 55 5.2 Key Indicators Represent Operation Opportunities 56 5.2.1 Reaction Optimization 56 5.2.2 Heat Exchanger Operation Optimization 58 5.2.3 Furnace Operation 58 5.2.4 Rotating Equipment Operation 59 5.2.5 Minimizing Steam Let down Flows 59 5.2.6 Turndown Operation 59 5.2.7 Housekeeping Aspects 59 5.3 Define Key Indicators 60 5.3.1 Process Analysis and Economics Analysis 61 5.3.2 Understand the Constraints 61 5.3.3 Identify Qualitatively Potential Area of Opportunities 65 5.4 Case Study of Ethylene Glycol Plant to Identify the Key Performance Indicator 66 5.4.1 Methodology 66 5.4.2 Ethylene Oxide Reaction Section 67 5.4.2.1 Understand the Process 67 5.4.2.2 Understanding the Economics of the Process 68 5.4.2.3 Factors that can Change the Production Cost and Overall Profit Generated from this Section 69 5.4.2.4 How is Production Cost Related to Process Parameters from the Standpoint of the Cause and Effect Relationship? 69 5.4.2.5 Constraints 69 5.4.2.6 Key Parameter Identifications 70 5.4.3 Cycle Water System 71 5.4.3.1 Main Purpose 71 5.4.3.2 Economics of the Process 71 5.4.3.3 Factors that can Change the Production Cost of this Section 72 5.4.3.4 Constraints 72 5.4.3.5 Key Performance Parameters 72 5.4.4 Carbon Dioxide Removal Section 73 5.4.4.1 Main Purpose 73 5.4.4.2 Economics 73 5.4.4.3 Factors that can Change the Production Cost of this Section 73 5.4.4.4 Constraints 74 5.4.4.5 Key Performance Parameters 74 5.4.5 EG Reaction and Evaporation Section 74 5.4.5.1 Main Purpose 74 5.4.5.2 Economics 75 5.4.5.3 Factors that can Change the Production Cost of this Section 76 5.4.5.4 Key Performance Parameters 76 5.4.6 EG Purification Section 76 5.4.6.1 Main Purpose 76 5.4.6.2 Economics 77 5.4.6.3 Key Performance Parameters 77 5.5 Purpose to Develop Key Indicators 77 5.6 Set up Targets for Key Indicators 78 5.7 Cost and Profit Dashboard 78 5.7.1 Development of Cost and Profit Dashboard to Monitor the Process Performance in Money Terms 78 5.7.2 Connecting Key Performance Indicators in APC 79 5.8 It is Crucial to Change the Viewpoints in Terms of Cost or Profit 80 References 80 6 Assessment of Current Plant Status 83 6.1 Introduction 83 6.1.1 Data Extraction from a Data Historian 83 6.1.2 Calculate the Economic Performance of the Section 84 6.2 Monitoring Variations of Economic Process Parameters 90 6.3 Determination of the Effect of Atmosphere on the Plant Profitability 90 6.4 Capacity Variations 91 6.5 Assessment of Plant Reliability 91 6.6 Assessment of Profit Suckers and Identification of Equipment for Modeling and Optimization 91 6.7 Assessment of Process Parameters Having a High Impact on Profit 93 6.8 Comparison of Current Plant Performance Against Its Design 93 6.9 Assessment of Regulatory Control System Performance 94 6.9.1 Basic Assessment Procedure 96 6.10 Assessment of Advance Process Control System Performance 97 6.11 Assessment of Various Profit Improvement Opportunities 97 References 98 7 Process Modeling by the Artificial Neural Network 99 7.1 Introduction 99 7.2 Problems to Develop a Phenomenological Model for Industrial Processes 100 7.3 Types of Process Model 101 7.3.1 First Principle-Based Model 101 7.3.2 Data-Driven Models 101 7.3.3 Grey Model 101 7.3.4 Hybrid Model 101 7.4 Emergence of Artificial Neural Networks as One of the Promising Data-Driven Modeling Techniques 106 7.5 ANN-Based Modeling 106 7.5.1 How Does ANN Work? 106 7.5.2 Network Architecture 107 7.5.3 Back-Propagation Algorithm (BPA) 107 7.5.4 Training 108 7.5.5 Generalizability 110 7.6 Model Development Methodology 110 7.6.1 Data Collection and Data Inspection 110 7.6.2 Data Pre-processing and Data Conditioning 110 7.6.2.1 Outlier Detection and Replacement 112 7.6.2.2 Univariate Approach to Detect Outliers 112 7.6.2.3 Multivariate Approach to Detect Outliers 112 7.6.3 Selection of Relevant Input–Output Variables 113 7.6.4 Align Data 113 7.6.5 Model Parameter Selection, Training, and Validation 113 7.6.6 Model Acceptance and Model Tuning 115 7.7 Application of ANN Modeling Techniques in the Chemical Process Industry 115 7.8 Case Study: Application of the ANN Modeling Technique to Develop an Industrial Ethylene Oxide Reactor Model 116 7.8.1 Origin of the Present Case Study 116 7.8.2 Problem Definition of the Present Case Study 117 7.8.3 Developing the ANN-Based Reactor Model 119 7.8.4 Identifying Input and Output Parameters 119 7.8.5 Data Collection 120 7.8.6 Neural Regression 121 7.8.7 Results and Discussions 122 7.9 Matlab Code to Generate the Best ANN Model 124 References 125 8 Optimization of Industrial Processes and Process Equipment 131 8.1 Meaning of Optimization in an Industrial Context 131 8.2 How Can Optimization Increase Profit? 132 8.3 Types of Optimization 133 8.3.1 Steady-State Optimization 133 8.3.2 Dynamic Optimization 133 8.4 Different Methods of Optimization 134 8.4.1 Classical Method 134 8.4.2 Gradient-Based Methods of Optimization 134 8.4.3 Non-traditional Optimization Techniques 135 8.5 Brief Historical Perspective of Heuristic-based Non-traditional Optimization Techniques 136 8.6 Genetic Algorithm 138 8.6.1 What is Genetic Algorithm? 138 8.6.2 Foundation of Genetic Algorithms 138 8.6.3 Five Phases of Genetic Algorithms 140 8.6.3.1 Initial Population 140 8.6.3.2 Fitness Function 140 8.6.3.3 Selection 140 8.6.3.4 Crossover 140 8.6.3.5 Termination 141 8.6.4 The Problem Definition 141 8.6.5 Calculation Steps of GA 141 8.6.5.1 Step 1: Generating Initial Population by Creating Binary Coding 141 8.6.5.2 Step 2: Evaluation of Fitness 142 8.6.5.3 Step 3: Selecting the Next Generation’s Population 142 8.6.6 Advantages of GA Against Classical Optimization Techniques 144 8.7 Differential Evolution 145 8.7.1 What is Differential Evolution (DE)? 145 8.7.2 Working Principle of DE 145 8.7.3 Calculation Steps Performed in DE 145 8.7.4 Choice of DE Key Parameters (NP, F, and CR) 145 8.7.5 Stepwise Calculation Procedure for DE implementation 146 8.8 Simulated Annealing 149 8.8.1 What is Simulated Annealing? 149 8.8.2 Procedure 149 8.8.3 Algorithm 150 8.9 Case Study: Application of the Genetic Algorithm Technique to Optimize the Industrial Ethylene Oxide Reactor 151 8.9.1 Conclusion of the Case Study 152 8.10 Strategy to Utilize Data-Driven Modeling and Optimization Techniques to Solve Various Industrial Problems and Increase Profit 153 References 155 9 Process Monitoring 159 9.1 Need for Advance Process Monitoring 159 9.2 Current Approaches to Process Monitoring and Diagnosis 160 9.3 Development of an Online Intelligent Monitoring System 161 9.4 Development of KPI-Based Process Monitoring 161 9.5 Development of a Cause and Effect-Based Monitoring System 163 9.6 Development of Potential Opportunity-Based Dash Board 163 9.6.1 Development of Loss and Waste Monitoring Systems 164 9.6.2 Development of a Cost-Based Monitoring System 165 9.6.3 Development of a Constraints-Based Monitoring System 166 9.7 Development of Business Intelligent Dashboards 166 9.8 Development of Process Monitoring System Based on Principal Component Analysis 167 9.8.1 What is a Principal Component Analysis? 168 9.8.2 Why Do We Need to Rotate the Data? 169 9.8.3 How Do We Generate Principal Components? 170 9.8.4 Steps to Calculating the Principal Components 170 9.9 Case Study for Operational State Identification and Monitoring Using PCA 171 9.9.1 Case Study 1: Monitoring a Reciprocating Reclaim Compressor 171 References 174 10 Fault Diagnosis 177 10.1 Challenges to the Chemical Industry 177 10.2 What is Fault Diagnosis? 178 10.3 Benefit of a Fault Diagnosis System 179 10.3.1 Characteristic of an Automated Fault Diagnosis System 180 10.4 Decreasing Downtime Through a Fault Diagnosis Type Data Analytics 180 10.5 User Perspective to Make an Effective Fault Diagnosis System 181 10.6 How Are Fault Diagnosis Systems Made? 183 10.6.1 Principal Component-Based Approach 184 10.6.2 Artificial Neural Network-Based Approach 184 10.7 A Case Study to Build a Robust Fault Diagnosis System 185 10.7.1 Challenges to a Build Fault Diagnosis of an Ethylene Oxide Reactor System 187 10.7.2 PCA-Based Fault Diagnosis of an EO Reactor System 187 10.7.3 Acquiring Historic Process Data Sets to Build a PCA Model 188 10.7.4 Criteria of Selection of Input Parameters for PCA 189 10.7.5 How PCA Input Data is Captured in Real Time 191 10.7.6 Building the Model 192 10.7.6.1 Calculations of the Principal Components 192 10.7.6.2 Calculations of Hotelling’s T2 192 10.7.6.3 Calculations of the Residual 193 10.7.7 Creation of a PCA Plot for Training Data 193 10.7.8 Creation of Hotelling’s T2 Plot for the Training Data 194 10.7.9 Creation of a Residual Plot for the Training Data 194 10.7.10 Creation of an Abnormal Zone in the PCA Plot 194 10.7.11 Implementing the PCA Model in Real Time 194 10.7.12 Detecting Whether the Plant is Running Normally or Abnormally on a Real-Time Basis 195 10.7.13 Use of a PCA Plot During Corrective Action in Real Time 197 10.7.14 Validity of a PCA Model 198 10.7.14.1 Time-Varying Characteristic of an EO Catalyst 198 10.7.14.2 Capturing the Efficiency of the PCA Model Using the Residual Plot 199 10.7.15 Quantitive Decision Criteria Implemented for Retraining of an Ethylene Oxide (EO) Reactor PCA Model 200 10.7.16 How Retraining is Practically Executed 200 10.8 Building an ANN Model for Fault Diagnosis of an EO Reactor 200 10.8.1 Acquiring Historic Process Data Sets to Build an ANN Model 200 10.8.2 Identification of Input and Output Parameters 201 10.8.3 Building of an ANN-Based EO Reactor Model 201 10.8.3.1 Complexity of EO Reactor Modeling 201 10.8.3.2 Model Building 202 10.8.4 Prediction Performance of an ANN Model 203 10.8.5 Utilization of an ANN Model for Fault Detection 203 10.8.6 How Do PCA Input Data Relate to ANN Input/Output Data? 204 10.8.7 Retraining of an ANN Model 206 10.9 Integrated Robust Fault Diagnosis System 206 10.10 Advantages of a Fault Diagnosis System 208 References 208 11 Optimization of an Existing Distillation Column 209 11.1 Strategy to Optimize the Running Distillation Column 209 11.1.1 Strategy 209 11.2 Increase the Capacity of a Running Distillation Column 210 11.3 Capacity Diagram 211 11.4 Capacity Limitations of Distillation Columns 212 11.5 Vapour Handling Limitations 214 11.5.1 Flow Regimes – Spray and Froth 214 11.5.2 Entrainment 215 11.5.3 Tray Flooding 215 11.5.4 Ultimate Capacity 217 11.6 Liquid Handling Limitations 217 11.6.1 Downcomer Flood 217 11.6.2 Downcomer Residence Time 217 11.6.3 Downcomer Froth Back-Up% 219 11.6.4 Downcomer Inlet Velocity 220 11.6.5 Weir liquid loading 221 11.6.6 Downcomer Sizing Criteria 221 11.7 Other Limitations and Considerations 221 11.7.1 Weeping 221 11.7.2 Dumping 222 11.7.3 Tray Turndown 222 11.7.4 Foaming 223 11.8 Understanding the Stable Operation Zone 223 11.9 Case Study to Develop a Capacity Diagram 224 11.9.1 Calculation of Capacity Limits 224 11.9.1.1 Spray Limit 224 11.9.1.2 Vapor Flooding Limit 226 11.9.1.3 Downcomer Backup Limit 226 11.9.1.4 Maximum Liquid Loading Limit 227 11.9.1.5 Minimum Liquid Loading Limit 227 11.9.1.6 Minimum Vapor Loading Limit 228 11.9.2 Plotting a Capacity Diagram 228 11.9.3 Insights from the Capacity Diagram 229 11.9.4 How Can the Capacity Diagram Be Used for Profit Maximization? 229 References 230 12 New Design Methodology 231 12.1 Need for New Design Methodology 231 12.2 Case Study of the New Design Methodology for a Distillation Column 231 12.2.1 Traditional Way to Design a Distillation Column 231 12.2.2 Background of the Distillation Column Design 232 12.3 New Intelligent Methodology for Designing a Distillation Column 234 12.4 Problem Description of the Case Study 237 12.5 Solution Procedure Using the New Design Methodology 237 12.6 Calculations of the Total Cost 238 12.7 Search Optimization Variables 239 12.8 Operational and Hydraulic Constraints 239 12.9 Particle Swarm Optimization 241 12.9.1 PSO Algorithm 241 12.10 Simulation and PSO Implementation 242 12.11 Results and Analysis 243 12.12 Advantages of PSO 245 12.13 Advantages of New Methodology over the Traditional Approach 246 12.14 Conclusion 248 Nomenclature 248 References 250 Appendix 12.1 251 13 Genetic Programing for Modeling of Industrial Reactors 259 13.1 Potential Impact of Reactor Optimization on Overall Profit 259 13.2 Poor Knowledge of Reaction Kinetics of Industrial Reactors 259 13.3 ANN as a Tool for Reactor Kinetic Modeling 260 13.4 Conventional Methods for Evaluating Kinetics 260 13.5 What is Genetic Programming? 261 13.6 Background of Genetic Programming 262 13.7 Genetic Programming at a Glance 263 13.7.1 Preparatory Steps of Genetic Programming 264 13.7.2 Executional Steps of Genetic Programming 264 13.7.3 Creating an Individual 267 13.7.4 Fitness Test 268 13.7.5 The Genetic Operations 269 13.7.6 User Decisions 271 13.7.7 Computing Resources 272 13.8 Example Genetic Programming Run 272 13.8.1 Preparatory Steps 273 13.8.2 Step-by-Step Sample Run 274 13.8.3 Selection, Crossover, and Mutation 275 13.9 Case Studies 277 13.9.1 Case Study 1 277 13.9.2 Case Study 2 278 13.9.3 Case Study 3 279 13.9.4 Case Study 4 280 References 281 14 Maximum Capacity Test Run and Debottlenecking Study 283 14.1 Introduction 283 14.2 Understanding Different Safety Margins in Process Equipment 283 14.3 Strategies to Exploit the Safety Margin 284 14.4 Capacity Expansion versus Efficiency Reduction 285 14.5 Maximum Capacity Test Run: What is it All About? 286 14.6 Objective of a Maximum Capacity Test Run 287 14.7 Bottlenecks of Different Process Equipment 288 14.7.1 Functional Bottleneck 288 14.7.2 Reliability Bottleneck 288 14.7.3 Safety Interlock Bottleneck 290 14.8 Key Steps to Carry Out a Maximum Capacity Test Run in a Commercial Running Plant 291 14.8.1 Planning 291 14.8.2 Discussion with Technical People 296 14.8.3 Risk and Opportunity 296 14.8.4 Dos and Don’ts 297 14.8.5 Simulations 298 14.8.6 Preparations 299 14.8.7 Management of Change 299 14.8.8 Execution 300 14.8.9 Data Collections 300 14.8.10 Critical Observations 302 14.8.11 Report Preparations 303 14.8.12 Detailed Simulations and Assembly of All Observations 303 14.8.13 Final Report Preparation 304 14.9 Scope and Phases of a Detailed Improvement Study 304 14.9.1 Improvement Scoping Study 305 14.9.2 Detail Feasibility Study 305 14.9.3 Retrofit Design Phase 305 14.10 Scope and Limitations of MCTR 306 14.10.1 Scope 306 14.10.2 Two Big Benefits of Doing MCTR 306 14.10.3 Limitations of MCTR 306 15 Loss Assessment 309 15.1 Different Losses from the System 309 15.2 Strategy to Reduce the Losses andWastages 309 15.3 Money Loss Audit 310 15.4 Product or Utility Losses 312 15.4.1 Loss in the Drain 312 15.4.2 Loss Due to Vent and Flaring 313 15.4.3 Utility Loss 314 15.4.4 Heat Loss Assessment for the Fired Heater 314 15.4.5 Heat Loss Assessment for the Distillation Column 315 15.4.6 Heat Loss Assessment for Steam Leakage 316 15.4.7 Heat Loss Assessment for Condensate Loss 317 16 Advance Process Control 319 16.1 What is Advance Process Control? 319 16.2 Why is APC Necessary to Improve Profit? 320 16.3 Why APC is Preferred over Normal PID Regulatory Control 322 16.4 Position of APC in the Control Hierarchy 324 16.5 Which are the Plants where Implementations of APC were Proven Very Profitable? 327 16.6 How do Implementations of APC Increase Profit? 328 16.7 How does APC Extract Benefits? 330 16.8 Application of APC in Oil Refinery, Petrochemical, Fertilizer and Chemical Plants and Related Benefits 334 16.9 Steps to Execute an APC Project 336 16.9.1 Step 1: Preliminary Cost –Benefit Analysis 336 16.9.2 Step 2: Assessment of Base Control Loops 337 16.9.3 Step 3: Functional Design of the Controller 337 16.9.4 Step 4: Conduct the Plant Step Test 338 16.9.5 Step 5: Generate a Process Model 338 16.9.6 Step 6: Commission the Online Controller 338 16.9.7 Step 7: Online APC Controller Tuning 339 16.10 How Can an Effective Functional Design Be Done? 339 16.10.1 Step 1: Define Process Control Objectives 340 16.10.2 Step 2: Identification of Process Constraints 342 16.10.3 Step 3: Define Controller Scope 343 16.10.4 Step 4: Variable Selection 344 16.10.5 Step 5: Rectify Regulatory Control Issues 346 16.10.6 Step 6: Explore the Scope of Inclusions of Inferential Calculations 347 16.10.7 Step 7: Evaluate Potential Optimization Opportunity 347 16.10.8 Step 8: Define LP or QP Objective Function 348 References 349 17 150 Ways and Best Practices to Improve Profit in Running Chemical Plant 351 17.1 Best Practices Followed in Leading Process Industries Around the World 351 17.2 Best Practices Followed in a Steam and Condensate System 351 17.3 Best Practices Followed in Furnaces and Boilers 355 17.4 Best Practices Followed in Pumps, Fans, and Compressor 357 17.5 Best Practices Followed in Illumination Optimization 359 17.6 Best Practices in Operational Improvement 359 17.7 Best Practices Followed in Air and Nitrogen Header 360 17.8 Best Practices Followed in Cooling Tower and CoolingWater 361 17.9 Best Practices Followed inWater Conservation 362 17.10 Best Practices Followed in Distillation Column and Heat Exchanger 363 17.11 Best Practices in Process Improvement 364 17.12 Best Practices in Flare Gas Reduction 365 17.13 Best Practices in Product or Energy Loss Reduction 365 17.14 Best Practices to Monitor Process Control System Performance 366 17.15 Best Practices to Enhance Plant Reliability 367 17.16 Best Practices to Enhance Human Resource 368 17.17 Best Practices to Enhance Safety, Health, and the Environment 368 17.18 Best Practices to Use New Generation Digital Technology 369 17.19 Best Practices to Focus a Detailed Study and R&D Effort 370 Index 373

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Book SynopsisThis is the most comprehensive and in-depth study of the theory and practical applications of a new and groundbreaking method for the energy industry to go green with renewable and alternative energy sources. The global warming phenomenon as a significant sustainability issue is gaining worldwide support for development of renewable energy technologies. The term polygeneration is referred to as an energy supply system, which delivers more than one form of energy to the final user. For example, electricity, cooling and desalination can be delivered from a polygeneration process. The polygeneration process in a hybrid solar thermal power plant can deliver electricity with less impact on the environment compared to a conventional fossil fuel-based power generating system. It is also THE next generation energy production technique with the potential to overcome the undesirable intermittence of renewable energy systems. In this study, the polygeneration process simulTable of ContentsContents Foreword ix Preface xi 1. Introduction 1 1.1. Global Scenario on Renewable Energy 3 1.2. Indian Scenario on Renewable Energy 6 Exercise 8 References 9 2. State-of-the-Art Concentrated Solar Thermal Technologies for End Use Applications 11 2.1. Solar Thermal Technologies for Low Grade Heat Applications 11 2.1.1. Flat Plate Collector System 12 2.1.2. Built-In Storage Solar Water Heating System 15 2.1.3. Evacuated Tubular Collector System 16 ETC Water Heating System Specification 18 2.1.4. Cumulative Growth of SWHS Installation Capacity 20 2.1.5. Performance Evaluation of SWHs 20 2.1.6. Cost Benefits Analysis 23 2.2. Solar Cooking 25 2.2.1. Thermal Performance of Solar Box Type Cooker 30 2.3. Solar Thermal Cooling 35 2.4. Desalination System 38 2.5. Industrial Process Heat applications 45 2.6. Solar Thermal Technologies for Power Generation 49 2.6.1. Parabolic Trough Collector 49 2.6.2. Linear Fresnel Reflector 51 2.6.3. Central Solar Tower 53 2.6.4. Parabolic Dish 54 2.7. Cooling with Process Heat in Cogeneration Process for Industrial Applications 57 2.7.1. System Description 58 Exercise 61 References 62 3. Resource Assessment of Solar and Biomass for Hybrid Thermal Power Plant 69 3.1. Apparent Solar Time 70 3.2. Solar Angles 71 3.3. Solar Resources (DNI) In India 76 3.3.1. Solar DNI from Satellite and Ground Measured Data 76 3.3.2. DNI Assessment at NISE 78 3.4. Biomass Resources in India 81 3.5. Analysis of Solar DNI And Biomass Resources for Hybrid Power Plants 83 Exercise 106 References 106 4. Solar Thermal Power Plant 109 4.1. A Case Study of 1 MWe Solar Thermal Power Plant 122 4.2. Major Components 124 4.2.1. Parabolic Trough Collector 124 4.2.2. Linear Fresnel Reflector 125 4.2.3. Storage 127 4.2.4. Nitrogen Blanketing System 129 4.2.5. Heat Exchanger 129 4.2.6. Power Block 132 4.2.7. Balance of Plant-Utility Systems 134 4.3. Performance of the Plant 136 Exercise 161 References 162 5. Modeling and Simulation of Hybrid Solar and Biomass Thermal Power Plant 163 5.1. Modeling Approach of a Hybrid Solar-Biomass Thermal Power Plant 167 5.2. Thermodynamic Evaluation 168 5.2.1. Energy Evaluation 169 5.2.2. Exergy Evaluation 174 5.3. Analysis of Hybrid Solar and Biomass Thermal Power Plant 177 Exercise 181 References 182 6. Modeling, Simulation, Optimization and Cost Analysis of a Polygeneration Hybrid Solar Biomass System 187 6.1. Modeling Approach of Polygeneration Process in an HSB Thermal Power Plant 191 6.2. Thermodynamic Evaluation 193 6.2.1. Energy Evaluation 193 6.2.2. Exergy Evaluation 201 6.3. Primary Energy Savings on the Polygeneration Process in an HSB Thermal Power Plant 206 6.4. Optimization 207 6.4.1. Objective Functions 207 6.4.2. Decision Variable and Constraints 207 6.4.3. Genetic Algorithm (GA) 207 6.5. Cost Analysis 209 6.6. Analysis Of Polygeneration Process in an HSB Thermal Power Plant for Power, Cooling, and Desalination 211 6.7. Optimization of the Polygeneration System 216 6.8. Cost Analysis of a Polygeneration System 220 Exercise 224 References 226 Appendix 1 231 Nomenclature 231 Greek 233 Subscripts 233 Acronyms 234 Appendix 2. 237 EES Software Coding 237 Appendix 3. 253 Multiple Choice Questions (MCQ) with Answers. 253 Answers 274 About the Author 275 Index 277

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Book SynopsisSustainable Manufacturing Systems Learn more about energy efficiency in traditional and advanced manufacturing settings with this leading and authoritative resource Sustainable Manufacturing Systems: An Energy Perspective delivers a comprehensive analysis of energy efficiency in sustainable manufacturing. The book presents manufacturing modeling methods and energy efficiency evaluation and improvement methods for different manufacturing systems. It allows industry professionals to understand the methodologies and techniques being embraced around the world that lead to advanced energy management. The book offers readers a comprehensive and systematic theoretical foundation for novel manufacturing system modeling, analysis, and control. It concludes with a summary of the insights and applications contained within and a discussion of future research issues that have yet to be grappled with. Sustainable Manufacturing Systems answers the questions that energy customers, managers, decision mTable of ContentsAuthor Biography xv Preface xvii Acknowledgments xxiii List of Figures xxv Part I Introductions to Energy Efficiency in Manufacturing Systems 1 1 Introduction 3 1.1 Definitions and Practices of Sustainable Manufacturing 3 1.1.1 Current Status of Manufacturing Industry 3 1.1.2 Sustainability in the Manufacturing Sector and Associated Impacts 5 1.1.3 Sustainable Manufacturing Practices 10 1.2 Fundamental of Manufacturing Systems 12 1.2.1 Stages of Product Manufacturing 12 1.2.2 Classification of Manufacturing Systems 13 1.2.2.1 Job Shop 13 1.2.2.2 Project Shop 14 1.2.2.3 Cellular System 15 1.2.2.4 Flow Line 15 1.2.2.5 Continuous System 15 1.3 Problem Statement and Scope 18 Problems 19 References 19 2 Energy Efficiency in Manufacturing Systems 23 2.1 Energy Consumption in Manufacturing Systems 23 2.1.1 Energy and Power Basics 23 2.1.2 Energy Generation 24 2.1.2.1 Primary Energy 25 2.1.2.2 Secondary Energy 27 2.1.3 Energy Distribution 27 2.1.3.1 Electricity 28 2.1.3.2 Steam 30 2.1.3.3 Compressed Air 30 2.1.4 Energy Consumption 31 2.1.4.1 Indirect End Use 33 2.1.4.2 Direct Process End Use 33 2.1.4.3 Direct Non-process End Use 34 2.2 Energy Saving Potentials and Energy Management Strategies for Manufacturing Systems 35 2.2.1 Machine Level 39 2.2.1.1 Intrinsic Characteristics of Machine Tools 41 2.2.1.2 Processing Conditions 42 2.2.2 System Level 43 2.2.2.1 Inhomogeneous System 44 2.2.2.2 Machine Maintenance 45 2.2.3 Plant Level 46 2.2.3.1 Indirect End Use 46 2.2.3.2 Direct Non-process End Use 47 2.3 Demand-side Energy Management 49 2.3.1 Electricity Bill Components 50 2.3.1.1 Electricity Cost 51 2.3.1.2 Demand Cost 51 2.3.1.3 Fixed Cost 52 2.3.2 Energy Efficiency Programs 52 2.3.3 Demand Response Programs 55 2.3.3.1 Incentive-based Programs 56 2.3.3.2 Price Base Options 57 Problems 59 References 59 Part II Mathematical Tools and Modeling Basics 65 3 Mathematical Tools 67 3.1 Probability 67 3.1.1 Fundamentals of Probability Theory 67 3.1.1.1 Basics of Probability Theory 67 3.1.1.2 Axioms of Probability Theory 69 3.1.1.3 Conditional Probability and Independence 72 3.1.1.4 Total Probability Theorem 73 3.1.1.5 Bayes’ Law 74 3.1.2 Random Variables 74 3.1.2.1 Discrete Random Variables 75 3.1.2.2 Continuous Random Variables 82 3.1.3 Random Process 88 3.1.3.1 Discrete-time Markov Chain 89 3.1.3.2 Continuous-time Markov Chain 92 3.2 Petri Net 94 3.2.1 Formal Definition of Petri Net 95 3.2.1.1 Definition of Petri Net 95 3.2.2 Classical Petri Net 99 3.2.2.1 State Machine Petri Net 101 3.2.2.2 Marked Graph 102 3.2.2.3 Systematic Modeling Methods 105 3.2.3 Deterministic Timed Petri Net 106 3.2.4 Stochastic Petri Net 109 3.3 Optimization Methods 113 3.3.1 Fundamentals of Optimization 113 3.3.1.1 Objective Function 114 3.3.1.2 Decision Variables 114 3.3.1.3 Constraints 115 3.3.1.4 Local and Global Optimum 116 3.3.1.5 Near-optimal Solutions 117 3.3.1.6 Single-objective and Multi-objective Optimization 117 3.3.1.7 Deterministic and Stochastic Optimization 118 3.3.2 Genetic Algorithms 119 3.3.2.1 Initialization 119 3.3.2.2 Evaluation 121 3.3.2.3 Selection 121 3.3.2.4 Crossover 123 3.3.2.5 Mutation 124 3.3.2.6 Termination Criteria 125 3.3.3 Particle Swarm Optimizer (PSO) 126 3.3.3.1 Initialization 126 3.3.3.2 Evaluation 128 3.3.3.3 Personal and Global Best Positions 128 3.3.3.4 Updating Velocity and Position 129 3.3.3.5 Termination Criteria 132 Problems 132 References 134 4 Mathematical Modeling of Manufacturing Systems 139 4.1 Basics in Manufacturing System Modeling 139 4.1.1 Structure of Manufacturing Systems 139 4.1.1.1 Basic Components 139 4.1.1.2 Structural Modeling 140 4.1.1.3 Types of Manufacturing Systems 141 4.1.2 Mathematical Models of Machines and Buffers 142 4.1.2.1 Timing Issues for Machines 143 4.1.2.2 Machine Reliability Models 143 4.1.2.3 Parameters of Aggregated Machines 145 4.1.2.4 Mathematical Model of Buffers 146 4.1.2.5 Interaction Between Machines and Buffers 147 4.1.2.6 Buffer State Transition 147 4.1.2.7 Blockage and Starvation 148 4.1.3 Performance Measures 150 4.1.3.1 Blockage and Starvation 150 4.1.3.2 Production Rate and Throughput 151 4.1.3.3 Work-in-process 151 4.2 Two-machine Production Lines 152 4.2.1 Conventions and Notations 152 4.2.1.1 Assumptions 152 4.2.1.2 Notations 152 4.2.2 State Transition 154 4.2.2.1 State Transition Probabilities 155 4.2.2.2 System Dynamics 157 4.2.3 Steady-state Probabilities 157 4.2.3.1 Identical Machines 159 4.2.3.2 Nonidentical Machines 160 4.2.4 Performance Measures 161 4.2.4.1 Blockage and Starvation 161 4.2.4.2 Production Rate 161 4.2.4.3 Work-in-process 162 4.3 Multi-machine Production Lines 162 4.3.1 Assumptions and Notations 163 4.3.1.1 Assumptions 163 4.3.1.2 Notations 163 4.3.2 State Transition 164 4.3.2.1 State Transition Probabilities 165 4.3.2.2 System Dynamics 167 4.3.3 Performance Measures 167 4.3.3.1 Blockage and Starvation 167 4.3.3.2 Production Rate 168 4.3.3.3 Work-in-process 169 4.3.4 System Modeling with Iteration-based Method 169 4.4 Production Lines Coupled with Material Handling Systems 174 4.4.1 Assumptions and Notations 174 4.4.1.1 Assumptions 175 4.4.1.2 Notations 175 4.4.2 State Transition and Performance 175 4.4.2.1 Blockage and Starvation 175 4.4.2.2 Production Rate 176 Problems 179 References 180 5 Energy Efficiency Characterization in Manufacturing Systems 181 5.1 Energy Consumption Modeling 181 5.1.1 Operation-based Energy Modeling 182 5.1.2 Component-based Energy Modeling 185 5.1.3 System-level Energy Modeling 188 5.2 Energy Cost modeling 191 5.2.1 Energy Cost Under Flat Rate 192 5.2.1.1 Energy Consumption Cost 192 5.2.1.2 Demand Cost 192 5.2.2 Energy Cost Under Time-of-use Rate 196 5.2.2.1 Energy Consumption Cost 196 5.2.2.2 Demand Cost 198 5.2.3 Energy Cost Under Critical Peak Price (CPP) 199 5.2.3.1 Energy Consumption Cost 199 5.2.3.2 Demand Cost 200 Problems 203 References 203 Part III Energy Management in Typical Manufacturing Systems 205 6 Electricity Demand Response for Manufacturing Systems 207 6.1 Time-of-use Pricing for Manufacturing Systems 208 6.1.1 Introduction to TOU 208 6.1.2 Survey of TOU Pricing in US Utilities 209 6.1.3 Comparison of Energy Cost Between Flat Rate and TOU Rates 210 6.2 TOU-Based Production Scheduling for Manufacturing Systems 216 6.2.1 Manufacturing Systems Modeling 216 6.2.2 Energy Consumption and Energy Cost Modeling 218 6.2.3 Production Scheduling for TOU-based Demand Response 219 6.2.3.1 Production Scheduling Problem Formulation 219 6.2.3.2 PSO Algorithm for Near-optimal Solutions 220 6.2.3.3 Case Study Setup 221 6.2.3.4 Optimal Production Schedules 222 6.3 Critical Peak Pricing for Manufacturing Systems 228 6.3.1 Introduction to Critical Peak Pricing (CPP) 228 6.3.2 Comparison of Energy Cost Between TOU and CPP Rates 229 Problems 234 Appendix 3.A Supplementary Information of Demand Response Tariffs 235 References 255 7 Energy Control and Optimization for Manufacturing Systems Utilizing Combined Heat and Power System 257 7.1 Introduction to Combined Heat and Power System 257 7.2 Problem Definition and Modeling 258 7.2.1 Objective Function 260 7.2.1.1 Electricity Cost 260 7.2.1.2 Operation Cost for the CHP System and Boiler 261 7.2.2 Constraints 262 7.3 Solution Approach 263 7.3.1 Initialization 263 7.3.2 Evaluation 264 7.3.3 Updating Process 265 7.4 Case Study 266 7.4.1 Case Study Settings 267 7.4.2 Results and Discussions 269 Problems 270 References 271 8 Plant-level Energy Management for Combined Manufacturing and HVAC System 273 8.1 Definition and Modeling 273 8.1.1 Objective Function 274 8.1.1.1 Calculate TEL(t) 276 8.1.1.2 Estimate q(t) 278 8.1.2 Constraints 279 8.2 Solution Approach 281 8.2.1 Initialization 281 8.2.2 Evaluation 282 8.2.3 Updating Process 282 8.3 Case Study 283 8.3.1 Model Settings 284 8.3.2 Results and Discussions 287 Problems 289 References 290 Part IV Energy Management in Advanced Manufacturing Systems 291 9 Energy Analysis of Stereolithography-based Additive Manufacturing 293 9.1 Introduction to Additive Manufacturing 293 9.1.1 Illustration of MIP SL-based AM Process 294 9.2 Energy Consumption Modeling 296 9.2.1 Energy Consumption of UV Curing Process 297 9.2.2 Energy Consumption of Building Platform Movement 298 9.2.3 Energy Consumption of Cooling System 298 9.3 Experimentation 298 9.3.1 Experiment Design Methodology 298 9.3.2 Experiment Apparatus 299 9.4 Results and Discussions 300 9.4.1 Baseline Case Results Using Default Conditions 300 9.4.2 Factorial Analysis Results 302 9.4.3 Product Quality Comparison 305 Problems 308 References 308 10 Energy Efficiency Modeling and Optimization of Cellulosic Biofuel Manufacturing System 311 10.1 Introduction to Cellulosic Biofuel Manufacturing 311 10.2 Energy Modeling of Cellulosic Biofuel Production 313 10.2.1 Energy Modeling of Biomass Size Reduction Process 314 10.2.2 Energy Modeling of Biofuel Chemical Conversion Processes 314 10.2.2.1 Heating Energy 315 10.2.2.2 Energy Loss 316 10.2.2.3 Reaction Energy 317 10.2.2.4 Energy Recovery 320 10.2.2.5 Total Energy Consumption 321 10.3 Energy Consumption Optimization Using PSO 321 10.3.1 Problem Formulation 321 10.3.2 Solution Procedures 322 10.3.2.1 Initialization 322 10.3.2.2 Evaluation 323 10.3.2.3 Updating Process 323 10.4 Case Study 323 10.4.1 Case Settings 324 10.4.2 Energy Analysis of Baseline Case 324 10.4.2.1 Energy Consumption Breakdown 324 10.4.3 Energy Analysis of Optimal Results 327 Problems 328 References 329 11 Energy-consumption Minimized Scheduling of Flexible Manufacturing Systems 333 11.1 Introduction 334 11.2 Construction of Place-timed PN for FMS Scheduling 335 11.2.1 Basic Definitions of PN 335 11.2.2 Place-timed PN Scheduling Models of FMS 336 11.3 Energy Consumption Functions 338 11.3.1 Calculating the Earliest Firing Time of Transitions 339 11.3.2 Two Energy Consumption Functions 340 11.3.2.1 Energy Consumption Function E1 341 11.3.2.2 Energy Consumption Function E2 341 11.4 Dynamic Programming for Scheduling FMS 344 11.4.1 Formulation of DP for FMSs 344 11.4.1.1 States and Stages 344 11.4.1.2 State Transition Equation 344 11.4.1.3 Bellman Equation 345 11.4.2 Reachability Graph of PNS 345 11.4.3 DP Implementation for Scheduling FMS 347 11.5 Modified Dynamic Programming for Scheduling FMS 348 11.5.1 Evaluation Function of Transition Sequences 349 11.5.2 Heuristic Function 350 11.5.3 MDP Algorithm for FMS Scheduling 351 11.6 Case Study 353 11.7 Summary 358 Problems 358 References 359 Part V Summaries and Conclusions 363 12 Research Trends and Future Directions in Sustainable Industrial Development 365 12.1 Insights into Sustainable Industrial Development 365 12.2 Energy and Resource Efficiency in Manufacturing 366 12.2.1 Equipment Design 366 12.2.2 Smart Manufacturing 367 12.3 Industrial Symbiosis 369 12.4 Supply Chain Management 371 12.5 Circular Economy 373 12.6 Life Cycle Assessment 376 References 378 Glossary 387 Acronyms 391 Index 393

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Book SynopsisA comprehensive overview of current developments and applications in biofuels production Process Systems Engineering for Biofuels Development brings together the latest and most cutting-edge research on the production of biofuels. As the first book specifically devoted to process systems engineering for the production of biofuels, Process Systems Engineering for Biofuels Development covers theoretical, computational and experimental issues in biofuels process engineering. Written for researchers and postgraduate students working on biomass conversion and sustainable process design, as well as industrial practitioners and engineers involved in process design, modeling and optimization, this book is an indispensable guide to the newest developments in areas including: Enzyme-catalyzed biodiesel productionProcess analysis of biodiesel production (including kinetic modeling, simulation and optimization)The use of ultrasonification in biodiesel productionThermochemical processes for biomTable of ContentsList of Contributors xiii Series Preface xv Preface xvii 1 Introduction 1Adrián Bonilla-Petriciolet and Gade Pandu Rangaiah 1.1 Importance of Biofuels and Overview of their Production 1 1.2 Significance of Process Systems Engineering for Biofuels Production 3 1.2.1 Modeling of Physicochemical Properties of Thermodynamic Systems Related to Biofuels 4 1.2.2 Intensification of the Biomass Transformation Routes for the Production of Biofuels 5 1.2.3 Computer-Aided Methodologies for Process Modeling, Design, Optimization, and Control Including Supply Chain and Life Cycle Analyses 7 1.3 Overview of this Book 9 References 11 2 Waste Biomass Suitable as Feedstock for Biofuels Production 15Maria Papadaki 2.1 Introduction 15 2.1.1 The Need for Biofuels 15 2.1.2 Problem Definition 17 2.1.3 The Biomass Pool 18 2.2 Kinds of Feedstock 20 2.2.1 Spent Coffee Grounds 21 2.2.2 Lignocellulose Biomass 22 2.2.3 Palm, Olive, Coconut, Avocado, and Argan Oil Production Residues 25 2.2.4 Citrus 33 2.2.5 Grape Marc 36 2.2.6 Waste Oil and Cooking Oil 37 2.2.7 Additional Sources 38 2.3 Conclusions 40 Acknowledgment 40 References 40 3 Multiscale Analysis for the Exploitation of Bioresources: From Reactor Design to Supply Chain Analysis 49Antonio Sánchez, Borja Hernández, and Mariano Martín 3.1 Introduction 49 3.2 Unit Level 50 3.2.1 Short Cut Methods 50 3.2.2 Mechanistic Models 51 3.2.3 Rules of Thumb 56 3.2.4 Dimensionless Analysis 56 3.2.5 Surrogate Models 56 3.2.6 Experimental Correlations 59 3.3 Process Synthesis 60 3.3.1 Heuristic Based 60 3.3.2 Supestructure Optimization 61 3.3.3 Environmental Impact Metrics 65 3.3.4 Safety Considerations 66 3.4 The Product Design Problem 66 3.4.1 Product Design: Engineering Biomass 66 3.4.2 Blending Problems 68 3.5 Supply Chain Level 68 3.5.1 Introduction 68 3.5.2 Modeling Issues 70 3.6 Multiscale Links and Considerations 71 Acknowledgment 74 Nomenclature 74 References 75 4 Challenges in the Modeling of Thermodynamic Properties and Phase Equilibrium Calculations for Biofuels Process Design 85Roumiana P. Stateva and Georgi St. Cholakov 4.1 Introduction 85 4.2 Thermodynamic Modeling Framework: Elements, Structure, and Organization 86 4.3 Thermodynamics of Biofuel Systems 88 4.3.1 Phase Equilibria 88 4.3.2 Thermodynamic Models 90 4.4 Sources of Data for Biofuels Process Design 98 4.5 Methods for Predicting Data for Biofuels Process Design 102 4.5.1 Group Contribution Methods for Biofuels Process Design 103 4.5.2 Quantitative Structure–Property Relationships for Biofuels Process Design 105 4.6 Challenges for the Biofuels Process Design Methods 109 4.7 Influence of Uncertainties in Thermophysical Properties of Pure Compounds on the Phase Behavior of Biofuel Systems 112 4.8 Conclusions 114 Acknowledgment 114 Exercises 114 References 115 5 Up-grading ofWaste Oil: A Key Step in the Future of Biofuel Production 121Luigi di Bitonto and Carlo Pastore 5.1 Introduction 121 5.2 Physicochemical Pretreatments of Waste Oils: Removal of Contaminants 124 5.3 Direct Treatment and Conversion of FFAs into Methyl Esters 125 5.3.1 Homogeneous Catalysis: Brønsted and Lewis Acids 125 5.3.2 Heterogeneous Catalysis 127 5.3.3 Enzymatic Biodiesel Production 128 5.3.4 ILs Biodiesel Production 130 5.3.5 Use of Metal Hydrated Salts 133 5.4 Future Trends of the Pretreatments of Waste Oils 139 5.5 Conclusions 140 Acknowledgment 141 Abbreviations 141 References 142 6 Production of Biojet Fuel from Waste Raw Materials: A Review 149Ana Laura Moreno-Gómez, Claudia Gutiérrez-Antonio, Fernando Israel Gómez-Castro, and Salvador Hernández 6.1 Introduction 149 6.2 Waste Triglyceride Feedstock 150 6.3 Waste Lignocellulosic Feedstock 159 6.4 Waste Sugar and Starchy Feedstock 164 6.5 Main Challenges and Future Trends 165 6.6 Conclusions 167 Acknowledgments 167 References 167 7 Computer-Aided Design for Genetic Modulation to Improve Biofuel Production 173 Feng-Sheng Wang and Wu-Hsiung Wu 7.1 Introduction 173 7.2 Method 175 7.2.1 Flux Balance Analysis 175 7.2.2 Flux Variability Analysis 176 7.2.3 Minimization of Metabolic Adjustment 176 7.2.4 Regulatory On-Off Minimization 177 7.2.5 Optimal Strain Design Problem 177 7.3 Computer-Aided Strain Design Tool 179 7.4 Examples 181 7.4.1 E. coli Core Model 181 7.4.2 Genome-Scale Metabolic Model of E. coli iAF1260 183 7.5 Conclusions 185 Appendix 7.A: The SBP Program 187 References 187 8 Implementation of Biodiesel Production Process Using Enzyme-Catalyzed Routes 191Thalles Allan Andrade, Massimiliano Errico, and Knud Villy Christensen 8.1 Introduction 191 8.2 Biodiesel Production Routes: Chemical versus Enzymatic Catalysts 194 8.2.1 Chemical Catalysts 195 8.2.2 Enzymatic Catalysts 196 8.3 Optimal Reaction Conditions and Kinetic Modeling 198 8.3.1 Evaluation of the Reaction Conditions 199 8.3.2 Kinetic Modeling 201 8.4 Process Simulation and Economic Evaluation 205 8.5 Reuse of Enzyme for the Transesterification Reaction 210 8.5.1 Recovery of Eversa Transform by Means of Centrifugation 210 8.5.2 Recovery of Eversa Transform by Means of Ceramic Membranes 211 8.6 Environmental Impact and Final Remarks 215 Acknowledgments 217 Nomenclature 217 References 217 9 Process Analysis of Biodiesel Production – Kinetic Modeling, Simulation, and Process Design 221Bruna Ricetti Margarida, Wanderson Rogerio Giacomin-Junior, Luiz Fernando de Lima Luz Junior, Fernando Augusto Pedersen Voll, and Marcos Lucio Corazza 9.1 Introduction 221 9.1.1 Homogeneous-Based Reactions 222 9.1.2 Heterogeneous-Based Reactions 223 9.1.3 Enzyme-Catalyzed Reactions 224 9.1.4 Supercritical Route Reactions 224 9.1.5 Methanol or Ethanol for Biodiesel Synthesis 224 9.2 Getting Started with Aspen Plus V10 224 9.2.1 Pure Compounds 225 9.2.2 Mixture Parameters 229 9.3 Kinetic Study 232 9.3.1 Esterification Reaction 232 9.3.2 Experimental Reaction Data Regression 234 9.3.3 Transesterification Reaction 236 9.3.4 Supercritical Route 238 9.4 Process Design 239 9.4.1 Esterification Reaction 239 9.4.2 Methanol Recycling 243 9.4.3 Transesterification Reaction 244 9.4.4 Biodiesel Purification 245 9.4.5 Additional Resources 248 9.5 Energy and Economic Analysis 252 9.6 Concluding Remarks 254 Acknowledgment 255 Exercises 255 References 256 10 Process Development, Design and Analysis of Microalgal Biodiesel Production Aided by Microwave and Ultrasonication 259Dipesh S. Patle, Savyasachi Shrikhande, and Gade Pandu Rangaiah 10.1 Introduction 259 10.2 Process Development and Modeling 262 10.3 Sizing and Cost Analysis 272 10.4 Comparison with the WCO-Based Process of the Same Capacity 277 10.4.1 Biodiesel Process Using WCO as Raw Material 277 10.4.2 Comparative Analysis 277 10.5 Comparison with the Microalgae-Based Processes 280 10.6 Conclusions 280 Acknowledgment 281 Appendix 10.A 281 Exercises 282 References 282 11 Thermochemical Processes for the Transformation of Biomass into Biofuels 285Carlos J. Durán-Valle 11.1 Introduction 285 11.2 Biomass and Biofuels 288 11.3 Combustion 289 11.4 Gasification 290 11.4.1 Fixed Bed Gasification 291 11.4.2 Fluidized Bed Gasification 292 11.4.3 Dual Fluidized Bed Gasification 292 11.4.4 Hydrothermal Gasification 293 11.4.5 Supercritical Water Gasification 294 11.4.6 Plasma Gasification 294 11.4.7 Catalyzed Gasification 295 11.4.8 Fischer–Tropsch Synthesis 295 11.5 Liquefaction 296 11.6 Pyrolysis 296 11.6.1 Slow Pyrolysis 297 11.6.2 Fast Pyrolysis 297 11.6.3 Flash Pyrolysis 297 11.6.4 Catalytic Biomass Pyrolysis 303 11.6.5 Microwave Heating 304 11.6.6 Product Separation 304 11.7 Carbonization 305 11.8 Conclusions 308 Acknowledgments 309 References 309 12 Intensified Purification Alternative for Methyl Ethyl Ketone Production: Economic, Environmental, Safety and Control Issues 311Eduardo Sánchez-Ramírez, Juan José Quiroz-Ramírez, and Juan Gabriel Segovia-Hernández 12.1 Introduction 311 12.2 Problem Statement and Case Study 316 12.3 Evaluation Indexes and Optimization Problem 317 12.3.1 Total Annual Cost Calculation 319 12.3.2 Environmental Index Calculation 319 12.3.3 Individual Risk Index 320 12.3.4 Controllability Index Calculation 322 12.3.5 Multi-Objective Optimization Problem 323 12.4 Global Optimization Methodology 324 12.5 Results 325 12.6 Conclusions 335 Acknowledgments 335 Notation 335 References 336 13 Present and Future of Biofuels 341Juan Gabriel Segovia-Hernández, César Ramírez-Márquez, and Eduardo Sánchez-Ramírez 13.1 Introduction 341 13.2 Some Representative Biofuels 344 13.2.1 Bioethanol 344 13.2.2 Biodiesel 347 13.2.3 Biobutanol 348 13.2.4 Biojet Fuel 349 13.2.5 Biogas 351 13.3 Perspectives and Future of Biofuels 352 References 354 Index 357

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Book SynopsisRoot Cause Failure Analysis Provides the knowledge and failure analysis skills necessary for preventing and investigating process equipment failuresProcess equipment and piping systems are essential for plant availability and performance. Regularly exposed to hazardous service conditions and damage mechanisms, these critical plant assets can result in major failures if not effectively monitored and assessedpotentially causing serious injuries and significant business losses. When used proactively, Root Cause Failure Analysis (RCFA) helps reliability engineers inspect the process equipment and piping system before any abnormal conditions occur. RCFA is equally important after a failure happens: it determines the impact of a failure, helps control the resultant damage, and identifies the steps for preventing future problems.Root Cause Failure Analysis: A Guide to Improve Plant Reliability offers readers clear understanding of degradation mechanisms of procesTable of ContentsPart- A 1- Introduction 2-What Is Root Cause Analysis 3-Failure Analysis Process 4-Managing Human Error and Latent Error Part-B 5- Metallurgical Failure Analysis 6- Piping Failure -Causes and Cure 7-Bolted Joint Failure 8- Coupling Failure 9-Bearing Failure 10- Mechanical Seal Failure 11-Failure of Centrifugal Pump 12- Failure of Reciprocating Pump 13- Failure of Centrifugal Compressor 14- Failure of Reciprocating Compressor 15-Lubrication Related Failure 16-Steam Trapfailure 17- Proactive Measures to Avoid Failure

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Book SynopsisThe book makes the case for process safety and provides a brief overviews of the upstream industry and of CCPS Risk Based Process Safety. The majority of the book focuses on the concepts of implementing process safety in wells, onshore, offshore, and projects. Topics include Overview of Upstream Operations; Overview of Risk Based Process Safety (RBPS); Application of RBPS in Drilling, Completions, Work-Overs & Interventions, Application of RBPS in Onshore Production, Application of RBPS in Offshore Production, Application of RBPS to Engineering Design, Installation, and Construction, Future Developments in the FieldTable of ContentsList of Tables vii List of Figures ix Acronyms and Abbreviations xi Glossary xv Acknowledgments xxv Online Materials Accompanying this Book xxvii 1 An Introduction to Process Safety for Upstream 1 1.1 Background 1 1.2 Applicability of Process Safety to Upstream 2 1.3 Intended Audience 3 1.4 Why the Reader Should be Interested 4 1.5 Scope of This Book 7 1.6 Upstream Safety Performance 7 1.7 Summary 10 2 The Upstream Industry 13 2.1 Upstream Industry 13 2.2 Exploration Phase 21 2.3 Engineering Design, Construction and Installation 23 2.4 Production Phase 25 2.5 Well Workovers and Interventions 28 2.6 Decommissioning Phase 28 2.7 Defining “Barriers” 29 2.8 Overview of International Regulations 33 3 Overview of Risk Based Process Safety (RBPS) 37 3.1 Background 37 3.2 RBPS Summary 37 3.3 Conclusion 52 4 Application of Process Safety to Wells 53 4.1 Background 53 4.2 Well Constuction: Risks and Key Process Safety Measures 63 4.3 Applying Process Safety Methods in Well Construction 72 5 Application of Process Safety to Onshore Production 87 5.1 Background 87 5.2 Onshore Production Facilities: Risks and Key Process Safety Measures 91 5.3 Applying Process Safety Methods in Onshore Production 99 6 Application of Process Safety to Offshore Production 107 6.1 Background 107 6.2 Offshore Production Facilities: Risks and Key Process Safety Measures 111 6.3 Applying Process Safety Methods in Offshore Production 117 7 Application of Process Safety to Engineering Design, Construction and Installation 129 7.1 Background 129 7.2 Front End Loading 132 7.3 Detailed Design 137 7.4 Procurement and Construction 138 7.5 Commissioning and Startup of Facilities 140 8 Process Safety: Looking Forward 141 8.1 Looking Forward 141 8.2 Research Needs 142 8.3 Technical Advances 144 8.4 Vision for Upstream Process Safety 146 References 147 Index 155

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Book SynopsisAGITATOR DESIGN FOR GAS-LIQUID FERMENTERS AND BIOREACTORS Explore the basic principles and concepts of the design of agitation systems for fermenters and bioreactorsAgitator Design for Gas-Liquid Fermenters and Bioreactors delivers a concise treatment and explanation of how to design mechanically sound agitation systems that will perform the agitation process function efficiently and economically. The book covers agitator fundamentals, impeller systems, optimum power and air flow at peak mass transfer calculations, optimizing operation for minimum energy per batch, heat transfer surfaces and calculations, shaft seal considerations, mounting methods, mechanical design, and vendor evaluation.The accomplished author has created a practical and hands-on tool that discusses the subject of agitation systems from first principles all the way to implementation in the real world. Step-by-step processes are included throughout the book to assist engineers, chemists,Table of ContentsPreface xix Foreword xxi Foreword for Greg Benz xxiii 1 Purpose of Agitator Design 1 References 2 2 Major Steps in Successful Agitator Design 3 Define Process Results 3 Define Process Conditions 5 Choose Tank Geometry 6 Calculate Equivalent Power/Airflow Combinations for Equal Mass Transfer Rate 7 Choose Minimum Combined Power 7 Choose Shaft Speed; Size Impeller System to Draw Required Gassed Power 7 Decision Point: D/T and Gassing Factors OK? 8 Mechanical Design 8 Decision Point: Is the Mechanical Design Feasible? 8 Repeat to Find Lowest Cost 8 Repeat for Different Aspect Ratios 9 Repeat for Different Process Conditions 9 Finish 9 Summary of Chapter 10 List of Symbols 10 References 10 3 Agitator Fundamentals 11 Agitated Tank Terminology 11 Prime Mover 11 Reducer 13 Shaft Seal 13 Wetted Parts 13 Tank Dimensions 14 How Agitation Parameters Are Calculated 14 Reynolds Number 15 Power Number 16 Pumping Number 17 Dimensionless Blend Time 17 Aeration Number 18 Gassing Factor 18 Nusselt Number 18 Froude Number 19 Prandtl Number 19 Geometric Ratios 20 Baffle Number 20 Dimensionless Hydraulic Force 20 Thrust Number 21 Typical Dimensionless Number Curves 21 A Primer on Rheology 25 Newtonian Model 26 Pseudoplastic or Shear Thinning, Model (Aka Power Law Fluid) 27 Bingham Plastic 27 Herschel–Bulkley 27 Impeller Apparent Viscosity 29 A Bit of Impeller Physics 29 Summary of Chapter 31 List of Symbols 31 Greek Letters 32 References 32 4 Agitator Behavior under Gassed Conditions 35 Flooding 35 Kla Method 35 Power Draw Method 36 Visual Flow Pattern Method 37 Effect on Power Draw 38 Holdup 39 Example of Holdup Calculation 40 Holdup “War Story” 40 Variable Gas Flow Operation 40 Mechanical Effects 42 Summary of Chapter 42 List of Symbols 42 References 43 5 Impeller Types Used in Fermenters 45 Impeller Flow Patterns 45 Axial Flow 46 Radial Flow 47 Mixed Flow 47 Chaos Flow 48 Examples of Axial Flow Impellers 49 Low Solidity 49 High Solidity 52 Up-pumping vs. Down Pumping 55 Examples of Radial Flow Impellers 56 Straight Blade Impeller 56 Disc, aka Rushton, Turbines 57 Smith Turbines 62 CD-6 Turbine by Chemineer; aka Smith Turbine by Many Manufacturers 62 Deeply Concave Turbines 66 Deep Asymmetric Concave Turbine with Overhang (BT-6) 68 Examples of Mixed Flow Impellers 73 Examples of Chaos Impellers 74 Shear Effects 76 Specialty Impellers 78 Summary of Chapter 80 List of Symbols 80 References 81 6 Impeller Systems 83 Why Do We Need a System? 83 Reaction Engineering 83 Fermenter History 84 Steps to Impeller System Design 85 Choose Number of Impellers 86 Choose Placement of Impellers 86 Choose Type(s) of Impellers 87 Choose Power Split or Distribution Among Impellers 93 Choose D/T and/or Shaft Speed 93 D/T Effects with Variable Gas Flowrates 96 Conclusions on D/T Ratio 98 Design to Minimize Shear Damage 99 Sparger Design 100 Ring Sparger 100 Pre-dispersion 103 Fine Bubble Diffuser 104 Summary of Chapter 105 List of Symbols 106 References 106 7 Piloting for Mass Transfer 109 Why Pilot for Mass Transfer 109 Methods for Determining kla 112 Sulfite Method 112 Dynamic Method; aka Dynamic Gassing/Degassing Method 112 Steady-State Method; aka Mass Balance Method 113 Combined Dynamic and Steady-State Method 114 Equipment Needed for Scalable Data 114 Data Gathering Needs 120 Experimental Protocol 121 Summary of Chapter 128 List of Symbols 128 References 129 8 Power and Gas Flow Design and Optimization 131 What This Chapter Is about 131 Where We Are in Terms of Design 131 Design with no Data 131 Design with Limited Pilot Data 133 Design with Full Data 135 Choose Minimum Combined Power 136 State of Design Completion 141 Additional Considerations 142 Summary of Chapter 142 List of Symbols 142 References 142 9 Optimizing Operation for Minimum Energy Consumption per Batch 145 Purpose of This Chapter 145 Prerequisite 145 Conceptual Overview 145 Detailed Procedure 146 Minimizing Total Energy Usage 150 Practical Design 150 Additional Considerations 150 Summary of Chapter 152 List of Symbols 152 References 153 10 Heat Transfer Surfaces and Calculations 155 Purpose of This Chapter 155 Design Philosophy 155 Overview of the Problem 156 Heat Sources 156 Cooling Sources 157 Heat Exchange Surface Overview 158 Principle of Heat Transfer Calculation 164 Calculations By Type of Surface 166 Vessel Jacket, Agitated Side 166 Simple Unbaffled Jacket, Jacket Side 167 Dimple Jacket, Jacket Side 167 Half-Pipe Coil, Jacket Side 169 Helical Coil, Inside 171 Helical Coil, Process Side 171 Vertical Tube Bundle, Inside 173 Vertical Tube Bundle, Process Side 174 Plate Coil, Inside 175 Plate Coil, Process Side 176 Example Problem: Vertical Tube Bundle 176 Problem Statement 176 Problem Solution 177 Additional Consideration: Effect on Power Draw 182 Additional Consideration: Forces on Heat Exchange Surfaces Used as Baffles 183 Additional Consideration: Wall Viscosity 184 Additional Consideration: Effect of Gas 185 External Heat Exchange Loops 186 Summary of Chapter 187 List of Symbols 187 References 189 Further Readings 189 11 Gasses Other Than Air and Liquids Other Than Water 191 General Principle 191 Comments on Some Specific Gasses 191 Ammonia 191 Carbon Dioxide 192 Carbon Monoxide 192 Hydrogen 192 Methane 192 Oxygen 192 Economic Factors 192 Disposal Factors 193 Effects of Different Gasses on kla 193 Effects of Different Gasses on Driving Force 195 Operating Condition Effects 195 Constraints on Outlet Concentration 196 Safety 196 Liquids Other Than Water 198 Summary of Chapter 198 List of Symbols 198 References 199 12 Viscous Fermentation 201 General Background 201 Sources of Viscosity 201 Viscosity Models for Broths 202 Effect of Viscosity on Power Draw 203 Example Problem 204 Example Problem Answer 204 Effect of Viscosity on kla 205 Effect of Viscosity on Holdup 207 Effect of Viscosity on Blend Time 207 Effect of Viscosity on Flooding 209 Caverns 209 Estimating Cavern Size 211 Xanthan and Gellan Gums 212 Viscosity Models for Gums 213 Installation Survey 214 Effect of D/T and No. and Type of Impellers on Results in Xanthan Gum 217 Production Curve 218 Heat Transfer 218 All-Axial Impeller Design 218 Invisible Draft Tube vs. Axial/Radial Combination 222 Mycelial Broths 223 Typical Viscosity Model 224 Morphology Effects 224 Recommendations 225 Summary of Chapter 227 List of Symbols 227 References 228 13 Three Phase Fermentation 231 General Problem 231 Effect on Mass Transfer 231 Effect on Foam 233 Emulsion vs. Suspension 233 Complexity: How to Optimize Operation 233 Summary of Chapter 234 List of Symbols 234 References 234 14 Use of CFD in Fermenter Design 237 Purpose of This Chapter 237 Basic Theory 237 Methods of Presenting Data 239 Velocity Distribution 240 Cavern Formation 240 Blending Progress 242 Flow Around Coils 245 Bubble Size, kla, Holdup 247 DO Distribution 248 Summary of Chapter 250 List of Symbols 250 References 250 15 Agitator Seal Design Considerations 251 Introduction 251 Terminology 251 Main Functions of Fermenter Shaft Seals 252 Common Types of Shaft Seals 254 Material Considerations 265 Methods of Lubricating Seals 267 Seal Environmental Control and Seal Support System 267 Seal Life Expectations 272 Special Process Considerations 272 Summary of Chapter 275 Reference 275 16 Fermenter Agitator Mounting Methods 277 Introduction 277 Top Entering Methods 277 Direct Nozzle Mount 278 Beam Gear Drive Mount with Auxiliary Packing or Lip Seal; Beams Tied into Vessel Sidewall 281 Beam Gear Drive Mount with Auxiliary Mechanical Seal; Beams Tied into Vessel Sidewall 283 Beam Gear Drive Mount with Auxiliary Mechanical Seal; Beams Tied into Building Structure 284 Complete Drive and Seal Mount to Beams Tied into Vessel Sidewall, with Bellows Connector 285 Complete Drive and Seal Mount to Beams Tied into Building Structure, with Bellows Connector 287 Bottom Entering Methods 287 Direct Nozzle Mount 288 Floor Gear Drive Mount with Auxiliary Packing or Lip Seal 288 Floor Gear Drive Mount with Auxiliary Mechanical Seal 289 Floor Integrated Drive and Seal Mount with Bellows Connector 291 Summary of Chapter 292 References 292 17 Mechanical Design of Fermenter Agitators 293 Introduction 293 Impeller Design Philosophy 294 Discussion on Hydraulic Force 295 Shaft Design Philosophy 297 Shaft Design Based on Stress 298 Simple Example Problem 302 Sample Problem with Steady Bearing 304 Shaft Design Based On Critical Speed 304 Cantilevered Designs 306 Example Problem 308 Units with Steady Bearings 311 Solid Shaft vs. Hollow Shaft 315 Role of FEA in Overall Shaft Design-Simplified Discussion 319 Agitator Gear Drive Selection Concepts 319 Early History 320 Loads Imposed 320 Handle or Isolate Loads? 323 Handle Loads Option 1: Oversized Commercial Gear Drive 323 Handle Loads Option 2: Purpose-Built Agitator Drive 324 Isolate Loads Option 1: Hollow Quill Integrated Drive with Flexibly Coupled Extension Shaft 325 Isolate Loads Option 2: Outboard Support Bearing Module 328 Bearing Life Considerations 329 Noise Considerations 330 Torsional Natural Frequency 332 Important or Useful Mechanical Design Features 332 Summary of Chapter 333 List of Symbols 333 Greek Letters 334 References 334 18 Sanitary Design 335 Introduction 335 Definitions 336 Construction Principles 336 Wetted Parts Construction Methods 336 Welded Construction 336 In-Tank Couplings 338 Mounting Flange Area 341 Axial Impellers 344 Radial Impellers 345 Bolts and Nuts 347 Steady Bearings 348 Use of Castings, 3-D Printing 349 Polishing Methods and Measures1: Polishing vs. Burnishing 350 Polishing Methods and Measures2: Lay 351 Polishing Methods and Measures3: Roughness Average 353 Electropolish 355 Passivating 357 Effect on Mechanical Design 357 Summary of Chapter 357 Additional Sources of Information 358 List of Symbols 358 References 358 19 Aspect Ratio 359 Acknowledgment 359 Definition and Illustration of Aspect Ratio 359 What Is the Optimum Aspect Ratio? 360 Effects of Z/T on Cost and Performance at a Given Working Volume 361 Vessel Cost 361 Agitator Shaft Design Difficulty 361 Power Required for Mass Transfer 361 Agitator Cost 362 Airflow Requirements 362 Compressor Power 362 DO Uniformity 362 Heat Transfer Capability 363 Real Estate/Land Usage Issues 363 Building Codes; Noise 363 Illustrative Problem Number 1 363 Vessel Dimensions 364 Airflow and Power 366 Heat Transfer Data and Assumptions 367 Heat Transfer Results 369 Blend Time, DO Uniformity 371 Capital Cost (Agitator Plus Vessel Only) 372 Other Operating Costs 372 So What Is the Optimum Aspect Ratio for This Problem? 373 Illustrative Problem Number 2 373 Illustrative Problem Number 3 376 Summary of Chapter 380 List of Symbols 381 References 381 20 Vendor Evaluation 383 Product Considerations 383 Gear Drive Ruggedness 384 Design Technology 384 Impeller Selection 384 Shaft Design 385 Company Considerations 385 Reputation with Customers 385 Company Size 386 Years in Business 386 Years Under New Ownership 386 Employee Turnover 387 Vertical Integration 387 R&D Program and Publications 388 Depth of Application Engineering 389 Testing Laboratory 389 ISO Certification (Necessary vs Sufficient) 391 Quality Control Program (Not Lot Sample; 100%) 391 Rep vs Direct Sales (a Good Rep Annoys the Manufacturer) 392 Service Capability 393 Typical Delivery Times and Performance 393 Parts Availability 394 Price (Least Important) 395 Willingness to Work with Consultants 395 Vendor Audit Checklist 396 Use of an Outside Consultant 397 Summary of Chapter 399 List of Symbols 399 References 400 A. Appendix to Chapter 20 400 21 International Practices 401 Introduction 401 North America 401 Vendors 401 Design Practices 402 Selling/Buying Practices 402 Degree of Vertical Integration 403 Role of Design Firms 403 R&D 404 Culture 404 EU 405 Vendors 405 Design Practices 405 Selling/Buying Practices 405 Degree of Vertical Integration 406 Role of Design Firms 406 R&D 406 Culture 407 Japan 407 Vendors 407 Design Practices 407 Selling/Buying Practices 407 Degree of Vertical Integration 408 Role of Design Firms 408 R&D 408 Culture 408 China 409 Vendors 409 Design Practices 409 Selling/Buying Practices 411 Degree of Vertical Integration 412 Role of Design Firms 412 R&D 412 Culture 413 Summary of Chapter 413 Cultural Resources 413 Afterword 415 Index 417

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Book SynopsisTable of ContentsPreface xi Acknowledgments xiii Nomenclature xv About the Companion Website xix 1 Chemical Process Projects 1 1.1 The Process Plant Design Problem 1 1.2 Continuous and Batch Processes 2 1.3 New Design and Retrofit 3 1.4 Hazard Management in Process Plant Design 4 1.5 Project Phases 4 1.6 Chemical Process Projects – Summary 5 References 6 2 Process Economics 7 2.1 Capital Cost Estimates 7 2.2 Class 5 Capital Cost Estimates 8 2.3 Class 4 Capital Cost Estimates 9 2.4 Class 3 to Class 1 Capital Cost Estimates 15 2.5 Capital Cost of Retrofit 15 2.6 Annualized Capital Cost 16 2.7 Operating Cost 17 2.8 Economic Evaluation 20 2.9 Investment Criteria 23 2.10 Process Economics − Summary 23 Exercises 24 References 25 3 Development of Process Design Concepts 27 3.1 Formulation of Design Problems 27 3.2 Evaluation of Performance 27 3.3 Optimization of Performance 28 3.4 Approaches to the Development of Design Concepts 29 3.5 Screening Design Options 32 3.6 Influencing the Design as the Project Progresses 33 3.7 Development of Process Design Concepts – Summary 34 References 35 4 Heating Utilities 37 4.1 Process Heating and Cooling 38 4.2 Steam Heating 39 4.3 Water Treatment for Steam Generation 44 4.4 Steam Generation from the Combustion of Fuels 45 4.5 Steam Generation from Electrical Energy 48 4.6 Gas Turbines 50 4.7 Steam Turbines 51 4.8 Steam Distribution 55 4.9 Steam Heating Limits 64 4.10 Fired Heaters 64 4.11 Other Heat Carriers 68 4.12 Heating Utilities – Summary 74 Exercises 74 References 76 5 Cooling Utilities 77 5.1 Waste Heat Steam Generation 77 5.2 Once-Through Cooling Water Systems 77 5.3 Recirculating Cooling Water Systems 78 5.4 Air Coolers 80 5.5 Refrigeration 82 5.6 Choice of a Single Component Refrigerant for Compression Refrigeration 88 5.7 Mixed Refrigerants for Compression Refrigeration 89 5.8 Absorption Refrigeration 93 5.9 Indirect Refrigeration 93 5.10 Cooling Utilities − Summary 94 Exercises 95 References 96 6 Waste Treatment 97 6.1 Aqueous Emissions 97 6.2 Primary Wastewater Treatment Processes 101 6.3 Biological Wastewater Treatment Processes 104 6.4 Tertiary Wastewater Treatment Processes 109 6.5 Atmospheric Emissions 109 6.6 Treatment of Solid Particulate Emissions to Atmosphere 111 6.7 Treatment of VOC Emissions to Atmosphere 114 6.8 Treatment of Sulfur Emissions to Atmosphere 120 6.9 Treatment of Oxides of Nitrogen Emissions to Atmosphere 123 6.10 Treatment of Combustion Emissions to Atmosphere 124 6.11 Atmospheric Dispersion 127 6.12 Waste Treatment − Summary 128 Exercises 128 References 129 7 Reliability, Maintainability, and Availability Concepts 131 7.1 Reliability, Maintainability, and Availability 131 7.2 Reliability 133 7.3 Repairable and Non-repairable Systems 136 7.4 Reliability Data 139 7.5 Maintainability 141 7.6 Availability 143 7.7 Process Shut-down for Maintenance 144 7.8 Reliability, Maintainability, and Availability Concepts − Summary 145 Exercises 145 References 146 8 Reliability, Maintainability, and Availability of Systems 147 8.1 System Representation 147 8.2 Reliability of Series Systems 147 8.3 Reliability of Parallel Systems 149 8.4 Availability of Parallel Systems 153 8.5 Availability of Series Systems 153 8.6 Redundancy 156 8.7 k-out-of-n Systems 159 8.8 Common Mode Failure 161 8.9 Capacity 166 8.10 Reliability, Availability, and Capacity 169 8.11 Monte Carlo Simulation 169 8.12 Reliability, Maintainability, and Availability of Systems − Summary 172 Exercises 172 References 174 9 Storage Tanks 175 9.1 Feed, Product, and Intermediate Storage 175 9.2 Intermediate (Buffer) Storage and Process Availability 177 9.3 Optimization of Intermediate Storage 181 9.4 Storage Tanks − Summary 182 Exercise 182 References 183 10 Process Control Concepts 185 10.1 Control Objectives 185 10.2 The Control Loop 185 10.3 Measurement 186 10.4 Control Signals 187 10.5 The Controller 187 10.6 Final Control Element 191 10.7 Feedback Control 195 10.8 Cascade Control 197 10.9 Split Range Control 198 10.10 Limit and Selector Control 200 10.11 Feedforward Control 201 10.12 Ratio Control 204 10.13 Computer Control Systems 205 10.14 Digital Control 207 10.15 Safety Instrumented Systems 210 10.16 Alarms and Trips 211 10.17 Representation of Control Systems 211 10.18 Process Control Concepts – Summary 215 Exercise 215 References 216 11 Process Control – Flowrate and Inventory Control 217 11.1 Flowrate Control 217 11.2 Inventory Control of Individual Operations 217 11.3 Inventory Control of Series Systems 223 11.4 Inventory Control of Recycle Systems 226 11.5 Flowrate and Inventory Control – Summary 227 References 228 12 Process Control – Degrees of Freedom 229 12.1 Degrees of Freedom and Process Control 229 12.2 Degrees of Freedom for Process Streams 231 12.3 Individual Single-Phase Operations 233 12.4 Heat Transfer Operations with No Phase Change 237 12.5 Pumps and Compressors 241 12.6 Equilibrated Multiphase Operations 243 12.7 Control Degrees of Freedom for Overall Processes 246 12.8 Degrees of Freedom – Summary 256 Exercises 256 References 257 13 Process Control – Control of Process Operations 259 13.1 Pump Control 259 13.2 Compressor Control 262 13.3 Heat Exchange Control 267 13.4 Furnace Control 271 13.5 Flash Drum Control 274 13.6 Absorber and Stripper Control 274 13.7 Distillation Control 278 13.8 Reactor Control 291 13.9 Control of Process Operations – Summary 301 Exercises 301 References 302 14 Process Control – Overall Process Control 303 14.1 Illustrative Example of Overall Process Control Systems 303 14.2 Synthesis of Overall Process Control Schemes 310 14.3 Procedure for the Synthesis of Overall Process Control Schemes 311 14.4 Evolution of the Control Design 323 14.5 Process Dynamics 324 14.6 Overall Process Control – Summary 325 Exercises 325 References 328 15 Piping and Instrumentation Diagrams – Piping and Pressure Relief 329 15.1 Piping and Instrumentation Diagrams 329 15.2 Piping Systems 330 15.3 Pressure Relief 335 15.4 Relief Device Arrangements 338 15.5 Reliability of Pressure Relief Devices 341 15.6 Location of Relief Devices 345 15.7 P&ID Piping and Pressure Relief – Summary 346 Exercises 346 References 348 16 Piping and Instrumentation Diagrams – Process Operations 349 16.1 Pumps 349 16.2 Compressors 355 16.3 Heat Exchangers 359 16.4 Distillation 361 16.5 Liquid Storage 366 16.6 P&ID Process Operations – Summary 373 Exercises 373 References 374 17 Piping and Instrumentation Diagrams – Construction 375 17.1 Development of Piping and Instrumentation Diagrams 375 17.2 A Case Study 376 17.3 P&ID Construction – Summary 387 References 387 18 Materials of Construction 389 18.1 Mechanical Properties 389 18.2 Corrosion 392 18.3 Corrosion Allowance 393 18.4 Commonly Used Materials of Construction 393 18.5 Criteria for Selection of Materials of Construction 397 18.6 Materials of Construction – Summary 398 References 398 19 Mechanical Design 399 19.1 Stress, Strain, and Deformation 399 19.2 Combined Stresses 423 19.3 Spherical Vessels Under Internal Pressure 426 19.4 Cylindrical Vessels Under Internal Pressure 428 19.5 Design of Heads for Cylindrical Vessels Under Internal Pressure 431 19.6 Design of Vertical Cylindrical Pressure Vessels Under Internal Pressure 434 19.7 Design of Horizontal Cylindrical Pressure Vessels Under Internal Pressure 439 19.8 Buckling of Cylindrical Vessels Due to External Pressure and Axial Compression 445 19.9 Welded and Bolted Joints 448 19.10 Opening Reinforcements 451 19.11 Vessel Supports 453 19.12 Design of Flat-bottomed Cylindrical Vessels 462 19.13 Shell-and-Tube Heat Exchangers 463 19.14 Mechanical Design – Summary 464 Exercises 465 References 467 20 Process Plant Layout − Site Layout 469 20.1 Site, Process, and Equipment Layout 469 20.2 Separation Distances 470 20.3 Separation for Vapor Cloud Explosions 472 20.4 Separation for Toxic Emissions 477 20.5 Site Access 477 20.6 Site Topology, Groundwater, and Drainage 479 20.7 Geotechnical Engineering 481 20.8 Atmospheric Discharges 481 20.9 Wind Direction 482 20.10 Utilities 483 20.11 Process Units 483 20.12 Control Room 483 20.13 Ancillary Buildings 485 20.14 Pipe Racks 485 20.15 Constraints on Site Layout 487 20.16 The Final Site Layout 487 20.17 Site Layout − Summary 487 References 487 21 Process Plant Layout − Process Layout 489 21.1 Process Access 489 21.2 Process Structures 489 21.3 Hazards 492 21.4 Preliminary Process Layout 492 21.5 Example – Preliminary Process Layout 493 21.6 Process Layout – Summary 498 References 498 Appendix A Weibull Reliability Function 499 Appendix B MTTF for the Weibull Distribution 501 Appendix C Reliability of Cold Standby Systems 503 Reference 504 Appendix D Corrosion Resistance Table 505 Appendix E Moment of Inertia and Bending Stress for Common Beam Cross-Sections 509 E.1 Solid Rectangular Cross-Section 509 E.2 Hollow Rectangular Cross-Section 509 E.3 Solid Circular Cylinder 510 E.4 Hollow Circular Cross-Section 511 E.5 Approximate Expressions for Thin-Walled Cylinders 511 Appendix F First Moment of Area and Shear Stress for Common Beam Cross-Sections 513 F.1 Solid Rectangular Cross-Section 513 F.2 Hollow Rectangular Cross-Section 513 F.3 Solid Circular Cross-Section 514 F.4 Hollow Circular Cross-Sections 515 Reference 515 Appendix G Principal Stresses 517 Appendix H Dimensions and Weights of Carbon Steel Pipes 521 Appendix I Bending Moment on Horizontal Cylindrical Vessels Resulting from a Liquid Hydraulic Head 525 References 526 Appendix J Equivalent Cylinder Approximation 527 Index 529

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Book SynopsisSustainable Separation Engineering Explore an insightful collection of resources exploring conventional and emerging materials and techniques for separations In Sustainable Separation Engineering: Materials, Techniques and Process Development, a team of distinguished chemical engineers delivers a comprehensive discussion of the latest trends in sustainable separation engineering. Designed to facilitate understanding and knowledge transfer between materials scientists and chemical engineers, the book is beneficial for scientists, practitioners, technologists, and industrial managers. Written from a sustainability perspective, the status and need for more emphasis on sustainable separations in the chemical engineering curriculum is highlighted. The accomplished editors have included contributions that explore a variety of conventional and emerging materials and techniques for efficient separations, as well as the prospects for the use of artificial intelliTable of ContentsAbout the Editors vii List of Contributors ix Preface xv Volume I 1 Electrochemically Mediated Sustainable Separations in Water 1 Kai-Jher Tan and T. Alan Hatton 2 Green and Sustainable Extraction of High-Value Compounds: Protein from Food Supply Chain Waste 63 Karine Zanotti, Aylon Matheus Stahl, Mateus Lodi Segatto, and Vânia Gomes Zuin 3 Separation Processes for Sustainable Produced Water Treatment and Management 105 Lanre M. Oshinowo, Young Chul Choi, Elaf A. Ahmed, and Hasan A. Al Abdulgader 4 Applications of Ultrasound in Separation Processes 155 Shankar B. Kausley, Gaurav G. Dastane, Rajshree A. Patil, Ananda J. Jadhav, Ketan S. Desai, and Aniruddha B. Pandit 5 The Role of Chemical Looping in Industrial Gas Separation199 Vedant Shah, Kalyani Jangam, Anuj Joshi, Pinak Mohapatra, Eric Falascino, and Liang-Shih Fan 6 Flow Technologies for Efficient Separations 239 Nopphon Weeranoppanant, Chetsada Khositanon, Trevor Murray, and Andrea Adamo 7 Sustainable Features of Centrifugal Partition Chromatography 261 Gergo ̋ Dargó and Árpád Könczöl 8 Liquid Membrane Technology for Sustainable Separations 297 Pablo López-Porfiri, María González-Miquel, and Patricia Gorgojo 9 Membrane-Enabled Sustainable Biofuel Production 343 Parimal Pal and Ramesh Kumar 10 Janus Membranes for Water Purification and Gas Separation 367 Jing Deng, Sepideh Razavi, and Michele Galizia Volume II 11 Adsorption Processes for Seawater Desalination 401 Qian Chen, Muhammad Burhan, Faheem Hassan Akhtar, Doskhan Ybyraiymkul, M. Kumja, Muhammad Ahmad Jamil, Muhammad Wakil Shahzad, and Kim Choon Ng 12 Sustainable Distillation Processes 431 Mirko Skiborowski, Kai Fabian Kruber, and Thomas Waltermann 13 Recovery of Solvents and Fine Chemicals 483 Yus Donald Chaniago and Moonyong Lee 14 Toward Green Extraction Processes 519 Marinela Nutrizio, Farid Chemat, Rattana Muangrat, Phisit Seesuriyachan, Yuthana Phimolsiripol, Francesco Donsi, and Anet Režek Jambrak 15 Cellulose Nanofibers for Sustainable Separations 563 Priyanka R. Sharma, Xiangyu Huang, Mengying Yang, Sunil K. Sharma, and Benjamin S. Hsiao 16 Recycling of Lithium Batteries 591 Mario Pagliaro and Francesco Meneguzzo 17 Deep Eutectic Solvents for Sustainable Separation Processes 605 Filipe H. B. Sosa, Mariana C. da Costa, Armando J. D. Silvestre, and André M. da Costa Lopes 18 Microfluidic Platforms for Cell Sorting 653 Fateme Mirakhorli, Seyed Sepehr Mohseni, Sajad Razavi Bazaz, Ali Abouei Mehrizi, Peter J. Ralph, and Majid Ebrahimi Warkiani 19 Sustainable Separations Using Organic Solvent Nanofiltration 697 Nazlee Faisal Ghazali and Ki Min Lim 20 Sustainable Separations in the Chemical Engineering Curriculum 731 Thomas Rodgers Index 741

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Book SynopsisPump Wisdom Explore key facets of centrifugal pump ownership, installation, operation, and troubleshooting The Second Edition of Pump Wisdom: Essential Centrifugal Pump Knowledge for Operators and Specialists delivers a concise explanation of how pumps function, the design specifications that must be considered before purchasing a pump, and current best practices in lubrication and mechanical seals. Readers will encounter new startup and surveillance tips for pump operators, as well as repair versus replacement or upgrade considerations for maintenance decision makers, new condition monitoring guidance for centrifugal pumps, and expanded coverage of operator best practices. This latest edition of Pump Wisdom: Essential Centrifugal Pump Knowledge for Operators and Specialists includes expanded coverage of areas critical to achieving best-in-class pump reliability, including commonly encountered issues and easy-to-follow instructions for getting centrifugal pumps to operate safely and reliably. This book also provides: Comprehensible and accessible explanations of pump hydraulicsSimple explorations of the mechanical aspects of pumps with coverage of bearings, seals, impeller trimming, lubricant application, and moreSafety tips and instructions for centrifugal pumps Perfect for chemical, petroleum, and mechanical engineers, Pump Wisdom: Essential Centrifugal Pump Knowledge for Operators and Specialists is also an ideal resource for operators, managers, purchasing agents, machinists, reliability technicians, and maintenance workers in water and wastewater plants.Table of ContentsPreface ix 1. Principles of Centrifugal Process Pumps 1 2. Pump Selection and Industry Standards 15 3. Foundations and Baseplates 23 4. Piping, Stationary Seals, and Gasketing 33 5. Rolling Element Bearings 51 6. Lubricant Application and Cooling Considerations 71 7. Lubricant Types and Key Properties 85 8. Bearing Housing Protection and Cost Justification 93 9. Mechanical Sealing Options for Long Life 101 10. Pump Operation 117 11. Impeller Modifications and Pump Maintenance 133 12. Lubrication Management 145 13. Pump Condition Monitoring: Pump Vibration, Rotor Balance, and Effect on Bearing Life 153 14. Drivers, Couplings, and Alignment 165 15. Fits, Dimensions, and Related Misunderstandings 175 16. Using Failure Statistics and Root Cause Analysis Findings to Guide Reliability Improvement Efforts 191 17. Repair, Replace, or Modify? 213 18. Centrifugal Pump Monitoring Strategies 231 19. Final Thoughts 249 Index 251

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Book SynopsisTable of ContentsPreface i 1 General Aspects 1 1.1 Subjects of the Book 1 1.2 Special Issues 2 1.3 Injection Molding 3 1.3.1 Cost Estimation in Injection Molding 3 1.3.2 Cost Prediction Models 4 1.4 Miniature Molding Processes 6 1.5 Computer Determination of Weld Lines in Injection Molding 6 1.6 Extrusion Blow Molding 8 1.6.1 Rapid Thermal Cycling Molding 8 1.6.2 Rapid Heat Cycle Molding 8 1.6.3 Injection Molding: Heating 16 1.7 Microcellular Injection Molding 22 1.8 Mold Cooling 23 1.9 Microcellular Foam Processing System 27 1.9.1 Gas-Assisted Injection Molding 27 1.9.2 Water-Assisted Injection Molding 32 1.10 Molding Machine for Granules 32 1.11 Foam Curing of Footwear 33 1.12 Injection Compression Molding 35 1.13 Hot Press System 35 1.14 Stamper Mold 38 1.14.1 Recoding Media 38 1.14.2 Microscopic Structured Body 39 1.15 Plastic Waste 42 1.15.1 Marine Pollution 43 1.15.2 Human Health Effects 45 1.15.3 Recycling 45 References 57 2 Process Analysis 65 2.1 Concepts and Strategies 66 2.1.1 Chemometrics 67 2.1.2 Safety Risks 68 2.1.3 Feedback Procedures 68 2.2 Linear Systems 68 2.2.1 Simple First-Order Systems 68 2.2.2 Fractional Order Systems 69 2.2.3 Nonlinear Systems and Linearization 69 2.2.4 Characteristics of Systems 75 2.2.5 Controllers and Controller Settings 84 2.3 Twin-Screw Extrusion 91 References 92 3 Examples of Process Analysis 99 3.1 Greenhouse Gas Balance 99 3.1.1 Poly(ethylene furandicarboxylate) 99 3.1.2 Polyester Binder 100 3.2 Injection Molding Technology 101 3.2.1 Module for CAD Modeling of the Part 103 3.2.2 Module forNumerical Simulation of Injection Molding Process 104 3.2.3 Module for Calculation of Parameters of Injection Molding and Mold Design Calculation and Selection 105 3.2.4 Module for Mold Modeling 106 3.2.5 Examples of Testing 107 3.2.6 Molding Air Cooling 108 3.2.7 Cavity Pressure 109 3.2.8 Plastics Extruder Dynamics 110 3.2.9 History of Mathematical Modeling 110 3.2.10 Current Physical Components Concept 112 3.2.11 Process Stages 112 3.2.12 Data Envelopment Analysis 116 3.2.13 Taguchi Method 118 3.2.14 Tait Model 119 3.2.15 Phan-Thien-Tanner Model 121 3.2.16 Product Quality Prognosis 121 3.2.17 Production Predictive Control 122 3.2.18 Parameter Optimization for Energy Saving 123 3.2.19 Multilayer Control System 124 3.2.20 Smoothed Particle Hydrodynamics Method 125 3.2.21 Temperature-Dependent Adaptive Control 126 3.2.22 Micro-Injection Molding 128 3.2.23 Immiscible Polymer Blends 131 3.2.24 Resin Injection Molding 133 3.2.25 Foam Injection Molding 137 3.2.26 Self-Optimizing Injection Molding Process 138 3.2.27 Machine Setup 140 3.3 Shrinkage in Injection Molding 146 3.3.1 Factors that Affect the Shrinkage 146 3.3.2 Effect of a Cooling System 147 3.3.3 Influence of Molding Conditions on the Shrinkage and Roundness 148 3.3.4 Shear Viscosity 148 3.3.5 In-Situ Shrinkage Sensor 149 3.3.6 Semicrystalline Polymer 151 3.3.7 Thermoplastic Elastomers 151 3.3.8 Reprocessing of ABS 153 3.3.9 Sequential Simplex Algorithm with Automotive Ventiduct Grid 155 3.3.10 Taguchi, ANOVA, CAE, and Neural Network Methods 156 3.4 Recycling by Extrusion 166 3.4.1 Multiple In-Line Extruders 166 3.4.2 Mixed Post-Consumer Plastic Waste 167 3.4.3 Poly(methyl methacrylate) 168 3.4.4 Poly(ethylene terephthalate) 169 3.4.5 Poly(lactic acid) 169 3.4.6 Expanded Poly(styrene) 169 3.5 Batch Washing of Recycled Films 171 3.5.1 Recycling of Poly(styrene)Waste 171 3.5.2 Textile Finishing 172 3.5.3 Removing Scrap from Containers 173 3.5.4 Adsorption Isotherms and Desorption Rates 175 3.6 Self-Purging Microwave Pyrolysis 176 3.7 Purging and Plasticization in Injection Molding 177 3.7.1 Automatic Purging 177 3.8 Hot Runner Systems 179 3.8.1 Hot Runner Mold with Runner Pipe 180 3.8.2 Hot Runner System in Plastics Molding Tools 183 3.8.3 Manufacturing and Assembling of Hot Runner Systems 184 3.9 Blown Film Extrusion and Thickness Control 185 3.10 Residence Time Distribution for Biomass Pyrolysis 186 3.11 Reactive Extrusion 187 References 187 4 Process Instrumentation 201 4.1 In-Mold Measurement 201 4.2 Temperature 202 4.2.1 Soft Actuator 202 4.2.2 Thermocouples 202 4.2.3 Resistance Temperature Detectors 206 4.2.4 Thin Film Miniature Temperature Sensors 214 4.2.5 Neural Networks 214 4.3 Position Transducers 215 4.3.1 Rotary Position Transducer 215 4.3.2 Linear Variable Differential Transformers 216 4.3.3 Optical Encoders 218 4.3.4 Thickness Gauges 218 4.4 Composition of Matter 222 4.4.1 IR Interferometer for Multilayer Film 222 4.4.2 X-Ray Diffraction 225 4.4.3 Ion Mobility-Mass Spectrometry 226 4.4.4 Test for Ice Adhesion Strength 226 4.4.5 Piezoelectric Coaxial Filament Sensors 228 4.4.6 Instrumentation for Impact Testing 228 4.4.7 Treatment of Titanium Surfaces 229 4.4.8 Spatial Differentiation of Sub-Micrometer Domains 230 4.5 Medical Issues 231 4.5.1 Endoscopic Plastic Surgical Procedures 231 4.5.2 Medical Catheters 231 4.5.3 Multichannel Plastic Joint 237 4.5.4 Transluminal Endoscopic Surgery 238 4.5.5 Wire-Actuated Universal-Joint Wrists 238 4.5.6 Musculoskeletal Disorders 239 References 240 5 Actuators and Final Control Elements 245 5.1 Servo Valves 245 5.1.1 Nozzle Assembly for a Servo Valve 245 5.2 Servo Motors 248 5.2.1 Hydraulic System 248 5.2.2 Functionally Graded Materials 248 5.3 Solenoid Valves 251 5.3.1 Design Verification Methodology 251 5.3.2 Small Solenoid Valve 252 5.3.3 High-Speed Solenoid Valve 252 5.3.4 Numerical Simulation 252 5.4 Heaters 253 5.4.1 Conduction Heaters 253 5.4.2 Radiant Heaters 255 5.4.3 Heater Controls 255 5.5 Drive Motors and Motor Speed Control for Extrusion 256 5.5.1 Single-Drive Motor 256 5.5.2 Linear Induction Motor 256 5.5.3 Motor Power Consumption in Single-Screw Extrusion 257 5.5.4 Dual Motor Multi-Head 3D Printer 258 References 258 6 Analysis of Melt Processing Systems 261 6.1 Process Parameter Determination of Plastic Injection Molding 261 6.1.1 Case-Based Reasoning Method 261 6.1.2 Knowledge-Based Reasoning Method 264 6.1.3 Rule-Based Reasoning Method 265 6.1.4 Fuzzy Reasoning Method 266 6.2 Process Parameter Determination of Plastic Injection Molding of LCDs 267 6.3 Processing History 267 6.3.1 Flow Defects 267 6.3.2 Biocomposites 269 6.3.3 3D Printing 271 6.3.4 Semiconducting Polymer Blends 272 6.3.5 Van Gurp-Palmen Plot 272 6.3.6 Nanocrystal Composites 273 6.3.7 Melt-Mastication 274 6.3.8 Crystal Nucleation in Nanocomposites 275 6.4 Shear History 276 6.5 Extrusion Product Control 278 6.5.1 Branched Structures 278 6.5.2 Big Area Additive Manufacturing 279 6.5.3 Single-Screw Extrusion Control 280 6.5.4 Blown Film 284 6.5.5 Chill Roll Cast Film 285 6.5.6 Sheet 292 6.5.7 Profiles 294 6.5.8 Pipe and Tubing 297 6.5.9 Automatic Screen Changers 303 6.6 Extrusion Blow Molding Parison Control 306 6.7 Injection Molding 310 6.7.1 Ram Velocity Control 310 6.7.2 Pressure Control 313 6.7.3 Gas-Assisted Control 319 6.7.4 System Diagnostics 322 6.7.5 Statistical Process and Quality Control 328 6.8 Thermoforming 329 6.8.1 Twin Sheet Thermoforming 329 6.8.2 Rotary Thermoforming 330 6.8.3 Process Model for Thermoforming 331 6.9 Rotomolding 332 6.9.1 Polymer Compositions for Rotomolding 334 6.10 Compounders 348 6.10.1 History of Compounding 348 6.10.2 Types of Compounders 348 6.10.3 Special Applications 350 References 352 7 Auxiliary Equipment 363 7.1 Crammer Feeder 363 7.1.1 Crammer Feeder for Extruder 363 7.1.2 Devulcanization of Scrap Rubber 363 7.2 Dryers 364 7.2.1 Drying Temperatures 364 7.2.2 Moisture Content 366 7.2.3 Resin Dryers 366 7.2.4 Pellet Dryers 369 7.3 Pullers 379 7.3.1 Pullers in Extrusion 379 7.3.2 Pullers in Injection Molding 381 7.4 Chillers 384 7.5 Robots 385 References 387 Index 389 Acronyms 389 Chemicals 394 General Index 399

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Book SynopsisUltra-Reliable and Low-Latency Communications (URLLC) Theory and Practice Comprehensive resource presenting important recent advances in wireless communications for URLLC services, including device-to-device communication, multi-connectivity, and more Ultra-Reliable and Low-Latency Communications (URLLC) Theory and Practice discusses the typical scenarios, possible solutions, and state-of-the-art techniques that enable URLLC in different perspectives from the physical layer to higher-level approaches, aiming to tackle URLLC's challenges with both theoretical and practical approaches, which bridges the lacuna between theory and practice. With long-term contributions to the development of future wireless networks, the text systematically presents a thorough study of the novel and innovative paradigm of URLLC; basic requirements are covered, along with essential definitions, state-of-the-art technologies, and promising research directions of URLLC. To aid in Table of ContentsPreface vii List of Contributors ix 1 URLLC: Faster, Higher, Stronger, and Together 1 Changyang She, Trung Q. Duong, Saeed R. Khosravirad, Petar Popovski, Mehdi Bennis, and Tony Q.S. Quek 2 Statistical Characterization of URLLC: Frequentist and Bayesian Approaches 15 Tobias Kallehauge, Pablo Ramirez-Espinosa, Anders E. Kalør, and Petar Popovski 3 Characterizing and Taming the Tail in URLLC 61 Chen-Feng Liu, Yung-Lin Hsu, Mehdi Bennis, and Hung-Yu Wei 4 Unsupervised Deep Learning for Optimizing Wireless Systems with Instantaneous and Statistic Constraints 85 Chengjian Sun, Changyang She, and Chenyang Yang 5 Channel Coding and Decoding Schemes for URLLC 119 Chentao Yue, Mahyar Shirvanimoghaddam, Branka Vucetic, and Yonghui li 6 Sparse Vector Coding for Ultra-reliable and Low-latency Communications 169 Byonghyo Shim 7 Network Slicing for URLLC 215 Peng Yang, Xing Xi, Tony Q. S. Quek, Jingxuan Chen, Xianbin Cao, and Dapeng Wu 8 Beamforming Design for Multi-user Downlink OFDMA-URLLC Systems 241 Walid R. Ghanem, Vahid Jamali, Yan Sun, and Robert Schober 9 A Full-Duplex Relay System for URLLC with Adaptive Self-Interference Processing 259 Hanjun Duan, Yufei Jiang, Xu Zhu, and Fu-Chun Zheng 10 Mobility Prediction for Reducing End-to-End Delay in URLLC 291 Zhanwei Hou, Changyang She, Yonghui Li, and Branka Vucetic 11 Relay Robot-Aided URLLC in 5G Factory Automation with Industrial IoTs 321 Dang Van Huynh, Saeed R. Khosravirad, Yuexing Peng, Antonino Masaracchia, and Trung Q. Duong Index 343

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Book SynopsisArtificial Intelligence in Process Fault Diagnosis A comprehensive guide to the future of process fault diagnosis Automation has revolutionized every aspect of industrial production, from the accumulation of raw materials to quality control inspections. Even process analysis itself has become subject to automated efficiencies, in the form of process fault analyzers, i.e., computer programs capable of analyzing process plant operations to identify faults, improve safety, and enhance productivity. Prohibitive cost and challenges of application have prevented widespread industry adoption of this technology, but recent advances in artificial intelligence promise to place these programs at the center of manufacturing process analysis. Artificial Intelligence in Process Fault Diagnosis brings together insights from data science and machine learning to deliver an effective introduction to these advances and their potential applications. Balancing theory and pracTable of ContentsList of Contributors xix Foreward xxi Preface xxiii Acknowledgements xxv 1 Motivations for Automating Process Fault Analysis 1 1.1 Introduction 2 1.2 The Changing Role of the Process Operators in Plant Operations 4 1.3 Traditional Methods for Performing Process Fault Management 7 1.4 Limitations of Human Operators in Performing Process Fault Management 8 1.5 The Role of Automated Process Fault Analysis 12 2 Various Process Fault Diagnostic Methodologies 16 2.1 Introduction 17 2.2 Various Alternative Diagnostic Strategies Overview 18 2.3 Diagnostic Methodology Choice Conclusions 35 2.A Failure Modes and Effects Analysis 40 3 Alarm Management and Fault Detection 45 3.1 Introduction 46 3.2 Applicable Definitions and Guidelines 46 3.3 The Alarm Management Life Cycle 49 3.4 Generation of Diagnostic Information 53 3.5 Presentation of the Diagnostic Information 55 3.6 Information Rates 59 4 Operator Performance: Simulation and Automation 63 4.1 Background 63 4.2 Automation 65 4.3 Simulation 68 4.4 Research 69 4.5 AI Integration 73 4.6 Case Study: Turbo Expanders Over-Speed 77 4.7 Human-Centered AI 80 5 AI and Alarm Analytics for Failure Analysis and Prevention 85 5.1 Introduction 86 5.2 Post-Alarm Assessment and Analysis 87 5.3 Real-Time Alarm Activity Database and Operator Action Journal 89 5.4 Pre-Alarm Assessment and Analysis 91 5.5 Utilizing Alarm Assessment Information 92 5.6 Examining the Alarm System to Resolve Failures on a Wider Scale 93 5.7 Emerging Methods of Alarm Analysis 99 5.8 Deep Reinforcement Learning for Alarming and Failure Assessment 103 5.9 Some Typical AI and Machine Learning Examples for Further Study 103 5.10 Wrap-Up 111 5.A Process State Transition Logic Employed by the Original FMC Falconeer KBS 112 5.B Process State Transition Logic and its Routine Use in Falconeer IV 123 6 Process Fault Detection Based on Time-Explicit Kiviat Diagram 131 6.1 Introduction 132 6.2 Time-Explicit Kiviat Diagram 133 6.3 Fault Detection Based on the Time-Explicit Kiviat Diagram 134 6.4 Continuous Processes 136 6.5 Batch Processes 138 6.6 Periodic Processes 140 6.7 Case Studies 141 6.8 Continuous Processes 141 6.9 Batch Processes 144 6.10 Periodic Processes 147 6.11 Conclusions 149 6.A Virtual Statistical Process Control Analysis 151 7 Smart Manufacturing and Real-Time Chemical Process Health Monitoring and Diagnostic Localization 160 7.1 Introduction to Process Operational Health Modeling 163 7.2 Diagnostic Localization – Key Concepts 165 7.3 Time 178 7.4 The Workflow of Diagnostic Localization 184 7.5 DL-CLA Use Case Implementation: Nova Chemical Ethylene Splitter 191 7.6 Analyzing Potential Malfunctions Over Time 198 7.7 Analysis of Various Operational Scenarios 201 7.8 DL-CLA Integration with Smart Manufacturing (SM) 208 7.9 AN FR Model Library 210 7.10 Conclusions 216 8 Optimal Quantitative Model-Based Process Fault Diagnosis 221 8.1 Introduction 222 8.2 Process Fault Analysis Concept Terminology 223 8.3 MOME Quantitative Models Overview 226 8.4 MOME Quantitative Model Diagnostic Strategy 234 8.5 MOME SV&PFA Diagnostic Rules’ Logic Compiler Motivations 248 8.6 MOME Fuzzy Logic Algorithm Overview 250 8.7 Summary of the Mome Diagnostic Strategy 265 8.8 Actual Process System KBS Application Performance Results 266 8.9 Conclusions 267 8.A Falconeer IV Fuzzy Logic Algorithm Pseudo-Code 272 8.B Mome Conclusions 281 9 Fault Detection Using Artificial Intelligence and Machine Learning 286 9.1 Introduction 287 9.2 Artificial Intelligence 287 9.3 Machine Learning 288 9.4 Engineered Features 290 9.5 Machine Learning Algorithms 291 10 Knowledge-Based Systems 300 10.1 Introduction 301 10.2 Knowledge 301 10.3 Information Required for Diagnosis 304 10.4 Knowledge Representation 305 10.5 Maintaining, Updating, and Extending Knowledge 309 10.6 Expert Systems 311 10.7 Digitization, Digitalization, Digital Transformation, and Digital Twins 319 10.8 Fault Diagnosis with Knowledge-Based Systems 322 10.9 Graphical Representation of Fault Diagnosis 325 10.10 Conclusions 337 10.A Compressor Trip Prediction 340 11 The Falcon Project 343 11.1 Introduction 344 11.2 The Diagnostic Philosophy Underlying the Falcon System 345 11.3 Target Process System 346 11.4 The Fielded Falcon System 348 11.5 The Derivation of the FALCON Diagnostic Knowledge Base 355 11.6 The Ideal FALCON System 369 11.7 Use of the Knowledge-Based System Paradigm in Problem 12 Fault Diagnostic Application Implementation and Sustainability 374 12.1 Key Principles of Successfully Implementing New Technology 375 12.2 Expectation of Advanced Technology 376 12.3 Defining Success 379 12.4 Learning from History 379 12.5 Example: Regulatory Control Loop Monitoring 380 12.6 What Success Looks Like 385 12.7 Example: Systematic Stewardship 386 12.8 Conclusions 387 13 Process Operators, Advanced Process Control, and Artificial Intelligence-Based Applications in the Control Room 389 13.1 Introduction 391 13.2 History of Sustainable APC 392 13.3 Operators as Ultimate APC Application End Users 394 13.4 APC Application Design Considerations 395 13.5 APC Development – Internal Versus External Experts 398 13.6 APC Technology 398 13.7 APC Support 400 13.8 Conclusions 402 References 402 Index 404

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Book SynopsisTable of ContentsPreface to the Third Edition xv Part I 1 1 Formulation of Physicochemical Problems 3 1.1 Introduction 3 1.2 Illustration of the Formulation Process (Cooling of Fluids) 3 1.2.1 Model I: Plug Flow 3 1.2.2 Model II: Parabolic Velocity 6 1.3 Combining Rate and Equilibrium Concepts (Packed-Bed Adsorber) 7 1.4 Boundary Conditions and Sign Conventions 8 1.5 Summary of the Model Building Process 9 1.6 Model Hierarchy and its Importance in Analysis 10 1.6.1 Level 1 10 1.6.2 Level 2 11 1.6.3 Level 3 13 1.6.4 Level 4 13 Problems 15 References 20 2 Modeling with Linear Algebra and Matrices 21 2.1 Introduction 21 2.2 Basic Concepts of Systems of Linear Equations 21 2.3 Matrix Notation 22 2.3.1 Matrices 22 2.3.2 Vectors 22 2.3.3 Scalars 22 2.3.4 Matrices and Vectors with Special Structure 22 2.4 Matrix Algebra and Calculus Operations 24 2.4.1 Equality 24 2.4.2 Addition and Subtraction 24 2.4.3 Multiplication 24 2.4.4 Division 26 2.4.5 Further Algebraic Properties of Matrices 27 2.4.6 Basic Differential and Integral Relations for Matrices 28 2.5 Problem 1: Solution of N Equations in N Unknowns 29 2.5.1 Analytical Results 29 2.5.2 Computational Approach: Gauss Elimination 30 2.6 Problem 2: The Matrix Eigenvalue Problem 32 2.6.1 Problem Statement and Formal Solution 32 2.6.2 Computing Eigensystems: Basic Procedure 33 2.7 Singular Systems 34 2.7.1 Consistent and Inconsistent Systems 34 2.7.2 Solution Structure for Consistent Systems 35 2.7.3 Formulation and Characteristics of Non-Square Problems 36 2.7.4 Over-Determined Systems: Least-Squares Solution 37 2.7.5 Under-Determined Systems 38 2.8 Computational Linear Algebra 40 2.8.1 The LU Factorization 40 2.8.2 The QR Factorization 40 2.8.3 The SVD Factorization 40 2.8.4 Large-Scale Problems and Iterative Methods 41 Problems 42 References 47 3 Solution Techniques for Models Yielding Ordinary Differential Equations 49 3.1 Geometric Basis and Functionality 49 3.2 Classification of ODE 50 3.3 First-Order Equations 50 3.3.1 Exact Solutions 51 3.3.2 Equations Composed of Homogeneous Functions 52 3.3.3 Bernoulli’s Equation 52 3.3.4 Riccati’s Equation 52 3.3.5 Linear Coefficients 54 3.3.6 First-Order Equations of Second Degree 54 3.4 Solution Methods for Second-Order Nonlinear Equations 55 3.4.1 Derivative Substitution Method 55 3.4.2 Homogeneous Function Method 58 3.5 Linear Equations of Higher Order 59 3.5.1 Second-Order Unforced Equations: Complementary Solutions 60 3.5.2 Particular Solution Methods for Forced Equations 64 3.5.3 Summary of Particular Solution Methods 70 3.6 Coupled Simultaneous ODE 71 3.7 Eigenproblems 74 3.8 Coupled Linear Differential Equations 74 3.9 Summary of Solution Methods for ODE 75 Problems 75 References 87 4 Series Solution Methods and Special Functions 89 4.1 Introduction to Series Methods 89 4.2 Properties of Infinite Series 90 4.3 Method of Frobenius 91 4.3.1 Indicial Equation and Recurrence Relation 91 4.4 Summary of the Frobenius Method 98 4.5 Special Functions 98 4.5.1 Bessel’s Equation 99 4.5.2 Modified Bessel’s Equation 100 4.5.3 Generalized Bessel’s Equation 100 4.5.4 Properties of Bessel Functions 102 4.5.5 Differential, Integral, and Recurrence Relations 103 Problems 105 References 107 5 Integral Functions 109 5.1 Introduction 109 5.2 The Error Function 109 5.2.1 Properties of Error Function 110 5.3 The Gamma and Beta Functions 110 5.3.1 The Gamma Function 110 5.3.2 The Beta Function 111 5.4 The Elliptic Integrals 111 5.5 The Exponential and Trigonometric Integrals 113 Problems 113 References 116 6 Staged-Process Models: The Calculus of Finite Differences 117 6.1 Introduction 117 6.1.1 Modeling Multiple Stages 117 6.2 Solution Methods for Linear Finite Difference Equations 118 6.2.1 Complementary Solutions 118 6.3 Particular Solution Methods 121 6.3.1 Method of Undetermined Coefficients 121 6.3.2 Inverse Operator Method 122 6.4 Nonlinear Equations (Riccati Equation) 122 Problems 124 References 126 7 Probability and Statistical Modeling 127 7.1 Concepts and Results From Probability Theory 127 7.1.1 Experiments and Random Variables 127 7.1.2 Probabilities and Distribution Functions 128 7.1.3 Characteristics of Distributions Functions 131 7.1.4 The Cumulative Distribution Function 132 7.2 Concepts and Results From Mathematical Statistics 134 7.2.1 Populations, Samples, and Sampling 134 7.2.2 Sample Statistics and Sampling Distributions 134 7.3 Statistical Analysis and Modeling 137 7.3.1 Confidence Interval for the Mean of a Population 137 7.3.2 Hypothesis Tests for the Population Mean 138 7.3.3 Hypothesis Tests: Comparing Multiple Means 140 7.3.4 Linear Models and Linear Regression 143 Problems 150 References 154 8 Approximate Solution Methods for ODE: Perturbation Methods 155 8.1 Perturbation Methods 155 8.1.1 Introduction 155 8.2 The Basic Concepts 157 8.2.1 Gauge Functions 157 8.2.2 Order Symbols 158 8.2.3 Asymptotic Expansions and Sequences 158 8.2.4 Sources of Nonuniformity 159 8.3 The Method of Matched Asymptotic Expansion 160 8.3.1 Outer Solutions 160 8.3.2 Inner Solutions 160 8.3.3 Matching 161 8.3.4 Composite Solutions 161 8.3.5 General Matching Principle 162 8.3.6 Composite Solution of Higher Order 162 8.4 Matched Asymptotic Expansions for Coupled Equations 163 8.4.1 Outer Expansion 163 8.4.2 Inner Expansion 164 8.4.3 Matching 164 Problems 165 References 173 Part II 175 9 Numerical Solution Methods (Initial Value Problems) 177 9.1 Introduction 177 9.2 Type of Method 179 9.3 Stability 180 9.4 Stiffness 185 9.5 Interpolation and Quadrature 186 9.6 Explicit Integration Methods 187 9.7 Implicit Integration Methods 188 9.8 Predictor–Corrector Methods and Runge–Kutta Methods 189 9.8.1 Predictor–Corrector Methods 189 9.9 Runge–Kutta Methods 189 9.10 Extrapolation 191 9.11 Step Size Control 192 9.12 Higher-Order Integration Methods 192 Problems 192 References 195 10 Approximate Methods for Boundary Value Problems: Weighted Residuals 197 10.1 The Method of Weighted Residuals 197 10.1.1 Variations on a Theme of Weighted Residuals 198 10.2 Jacobi Polynomials 205 10.2.1 Rodrigues Formula 205 10.2.2 Orthogonality Conditions 205 10.3 Lagrange Interpolation Polynomials 206 10.4 Orthogonal Collocation Method 206 10.4.1 Differentiation of a Lagrange Interpolation Polynomial 206 10.4.2 Gauss–Jacobi Quadrature 207 10.4.3 Radau and Lobatto Quadrature 208 10.5 Linear Boundary Value Problem: Dirichlet Boundary Condition 209 10.6 Linear Boundary Value Problem: Robin Boundary Condition 211 10.7 Nonlinear Boundary Value Problem: Dirichlet Boundary Condition 213 10.8 One-Point Collocation 215 10.9 Summary of Collocation Methods 215 10.10 Concluding Remarks 216 Problems 217 References 225 11 Introduction to Complex Variables and Laplace Transforms 227 11.1 Introduction 227 11.2 Elements of Complex Variables 227 11.3 Elementary Functions of Complex Variables 228 11.4 Multivalued Functions 229 11.5 Continuity Properties for Complex Variables: Analyticity 230 11.5.1 Exploiting Singularities 231 11.6 Integration: Cauchy’s Theorem 232 11.7 Cauchy’s Theory of Residues 233 11.7.1 Practical Evaluation of Residues 234 11.7.2 Residues at Multiple Poles 235 11.8 Inversion of Laplace Transforms by Contour Integration 235 11.8.1 Summary of Inversion Theorem for Pole Singularities 237 11.9 Laplace Transformations: Building Blocks 237 11.9.1 Taking the Transform 237 11.9.2 Transforms of Derivatives and Integrals 238 11.9.3 The Shifting Theorem 240 11.9.4 Transform of Distribution Functions 240 11.10 Practical Inversion Methods 242 11.10.1 Partial Fractions 242 11.10.2 Convolution Theorem 243 11.11 Applications of Laplace Transforms for Solutions of ODE 243 11.12 Inversion Theory for Multivalued Functions: The Second Bromwich Path 248 11.12.1 Inversion When Poles and Branch Points Exist 250 11.13 Numerical Inversion Techniques 250 11.13.1 The Zakian Method 250 11.13.2 The Fourier Series Approximation 252 Problems 253 References 257 12 Solution Techniques for Models Producing PDEs 259 12.1 Introduction 259 12.1.1 Classification and Characteristics of Linear Equations 261 12.2 Particular Solutions for PDEs 263 12.2.1 Boundary and Initial Conditions 263 12.3 Combination of Variables Method 264 12.4 Separation of Variables Method 269 12.4.1 Coated Wall Reactor 269 12.5 Orthogonal Functions and Sturm–Liouville Conditions 272 12.5.1 The Sturm–Liouville Equation 272 12.6 Inhomogeneous Equations 275 12.7 Applications of Laplace Transforms for Solutions of PDEs 279 Problems 285 References 302 13 Transform Methods for Linear PDEs 305 13.1 Introduction 305 13.2 Transforms in Finite Domain: Sturm–Liouville Transforms 305 13.2.1 Development of Integral Transform Pairs 306 13.2.2 The Eigenvalue Problem and the Orthogonality Condition 309 13.2.3 Inhomogeneous Boundary Conditions 313 13.2.4 Inhomogeneous Equations 316 13.2.5 Time-Dependent Boundary Conditions 317 13.2.6 Elliptic Partial Differential Equations 317 13.3 Generalized Sturm–Liouville Integral Transform 320 13.3.1 Introduction 320 13.3.2 The Batch Adsorber Problem 320 Problems 327 References 331 14 Approximate and Numerical Solution Methods for PDEs 333 14.1 Polynomial Approximation 333 14.2 Singular Perturbation 338 14.3 Finite Difference 343 14.3.1 Notations 343 14.3.2 Essence of the Method 344 14.3.3 Tridiagonal Matrix and the Thomas Algorithm 345 14.3.4 Linear Parabolic Partial Differential Equations 345 14.3.5 Nonlinear Parabolic Partial Differential Equations 349 14.4 Orthogonal Collocation for Solving PDEs 350 14.4.1 Elliptic PDE 350 14.4.2 Parabolic PDE: Example 1 353 14.4.3 Coupled Parabolic PDE: Example 2 354 Problems 355 References 362 Appendix A: Review of Methods for Nonlinear Algebraic Equations 363 A.1 The Bisection Algorithm 363 A.2 The Successive Substitution Method 364 A.3 The Newton–Raphson Method 366 A.4 Rate of Convergence 367 A.4.1 Definition of Speed of Convergence 367 A.5 Multiplicity 368 A.5.1 Multiplicity 368 A.6 Accelerating Convergence 369 References 369 Appendix B: Derivation of the Fourier–Mellin Inversion Theorem 371 References 374 Appendix C: Table of Laplace Transforms 375 Appendix D: Numerical Integration 381 D.1 Basic Idea of Numerical Integration 381 D.2 Newton Forward Difference Polynomial 381 D.3 Basic Integration Procedure 382 D.3.1 Trapezoid Rule 382 D.3.2 Simpson’s Rule 383 D.4 Error Control and Extrapolation 384 D.5 Gaussian Quadrature 384 D.6 Radau Quadrature 386 D.7 Lobatto Quadrature 388 D.8 Concluding Remarks 389 References 389 Appendix E: Nomenclature 391 Appendix F: Statistical Tables 395 Postface 399 Index 401

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Book SynopsisCHEMICAL PROCESS ENGINEERING Written by one of the most prolific and respected chemical engineers in the world and his co-author, also a well-known and respected engineer, this two-volume set is the new standard in the industry, offering engineers and students alike the most up-do-date, comprehensive, and state-of-the-art coverage of processes and best practices in the field today. This new two-volume set explores and describes integrating new tools for engineering education and practice for better utilization of the existing knowledge on process design. Useful not only for students, university professors, and practitioners, especially process, chemical, mechanical and metallurgical engineers, it is also a valuable reference for other engineers, consultants, technicians and scientists concerned about various aspects of industrial design. The text can be considered as complementary to process design for senior and graduate students as well as a hands-on reference work or refresher for engineers at entry level. The contents of the book can also be taught in intensive workshops in the oil, gas, petrochemical, biochemical and process industries. The book provides a detailed description and hands-on experience on process design in chemical engineering, and it is an integrated text that focuses on practical design with new tools, such as Microsoft Excel spreadsheets and UniSim simulation software. Written by two of the industry's most trustworthy and well-known authors, this book is the new standard in chemical, biochemical, pharmaceutical, petrochemical and petroleum refining. Covering design, analysis, simulation, integration, and, perhaps most importantly, the practical application of Microsoft Excel-UniSim software, this is the most comprehensive and up-to-date coverage of all of the latest developments in the industry. It is a must-have for any engineer or student's library.Table of ContentsPreface xxi Acknowledgments xxiii About the Authors xxv 8 Heat Transfer 505 9 Process Integration and Heat Exchanger Network 947 10 Process Safety and Pressure-Relieving Devices 1093 11 Chemical Kinetics and Reactor Design 1253 12 Engineering Economics 1335 13 Optimization in Chemical/Petroleum Engineering 1363 Epilogue 1405 Index 1415

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Book SynopsisTable of ContentsTable of Contents v List of Figures xi List of Tables xiii Acronyms and Abbreviations xvii Glossary xxiii Acknowledgments xxix Preface xxxiii Part 1: Introduction, Background, and History of Cybersecurity 1 1 Purpose of this Book 1 1.1 Target Audience 6 1.2 What is Cybersecurity? 6 1.3 What is Operational Technology (OT)? 10 1.4 Which industries have OT? 13 1.5 Scope 15 1.6 Organization of the Book 17 2 Types of Cyber-Attacks, Who Engages in Them and Why 19 2.1 Types of Cyber-Attacks 19 2.2 Who Commits Cybercrimes and Their Motives 26 2.3 Summary 30 3 Types of Risk Receptors / Targets 33 3.1 What is Cybersecurity Risk 35 3.2 What are Common Cybersecurity Targets? 38 3.3 Types of Cybersecurity Consequences 43 3.4 Summary 45 4 Threat Sources and Types of Attacks 47 4.1 Non-Targeted Attacks 49 4.2 Targeted Attacks 53 4.3 Advanced Persistent Threats (APT) 58 4.4 Summary 62 5 Who Could Create a Cyber Risk? Insider vs Outsider Threats 65 5.1 Insider Cybersecurity Risk 65 5.2 Outsider Cybersecurity Risk 69 5.3 Summary 71 6 Case Histories 73 6.1 Maroochy Shire 73 6.2 Stuxnet 77 6.3 German Steel Mill 81 6.4 Ukrainian Power Grid 84 6.5 NotPetya 91 6.6 Triton 95 6.7 Düsseldorf Hospital Ransomware 99 6.8 SolarWinds 101 6.9 Florida Water System 105 6.10 Colonial Pipeline Ransomware 107 6.11 Summary 110 Part 2: Integrating Cybersecurity Management into the Process Safety Framework 113 7 General Model for Understanding Cybersecurity Risk 113 7.1 Cybersecurity Lifecycle 113 7.2 Integrated Cybersecurity and Safety Lifecycle 121 7.3 NIST Cybersecurity Framework 129 7.4 Summary 138 8 Designing a Secure Industrial Automation and Control System 141 8.1 The Disconnect between IT and OT Risk Management 141 8.2 Inherently Safer vs Inherently More Secure 146 8.3 Defense-in-Depth 149 8.4 Network Segmentation 153 8.5 System Hardening 173 8.6 Security Monitoring 176 8.7 Risk Compatibility Assessment 180 8.8 Summary 182 9 Hazard Identification and Risk Analysis (HIRA) 183 9.1 Use of Process Safety Tools to Identify and Manage Cybersecurity Risk 185 9.2 Qualitative Methods 187 9.3 Quantitative Methods 217 9.4 How to Prioritize Risk Reduction Measures? 231 9.5 Revalidation/Reassessment 232 9.6 Summary 233 10 Manage the Risk 235 10.1 Management Approach 235 10.2 Initial Steps 236 10.3 Cybersecurity Culture 240 10.4 Compliance with Standards 242 10.5 Cybersecurity Competency 246 10.6 Workforce Involvement 248 10.7 Stakeholder Outreach 251 10.8 Process Knowledge Management 252 10.9 Operating Procedures 256 10.10 Safe Work Practices 259 10.11 Management of Change 262 10.12 Asset Integrity and Reliability 266 10.13 Contractor Management 272 10.14 Training and Performance Assurance 275 10.15 Operational Readiness 278 10.16 Conduct of Operations 281 10.17 Emergency Management 285 10.18 Incident Investigation 290 10.19 Measurements and Metrics 295 10.20 Auditing 300 10.21 Management Review and Continuous Improvement 304 10.22 Summary 307 11 Implementing a Holistic Approach to Safety and Cybersecurity 311 11.1 Cybersecurity Management Systems (CSMS) 312 11.2 Integrating CSMS with Process Safety Management 327 11.3 Summary 334 Part 3: Where Do We Go from Here? 337 12 What’s Next? A Look at Future Development Opportunities 337 12.1 Cybersecurity Adoption Trends 338 12.2 Emerging Technologies 350 12.3 Summary 353 13 Available Resources 355 13.1 Local, Regional, and Global Topics 355 13.2 Cybersecurity Incident Repositories 362 13.3 Competency Requirements and Training Availability 363 13.4 Administration vs Accountability Functions 368 13.5 Summary 370 Appendix A Excerpt from NIST Cybersecurity Framework 371 Appendix B Detailed Cybersecurity PHA and LOPA Example 377 B.1 System Basis 377 B.2 Initial Risk Assessment 382 B.3 Detailed Risk Assessment (Cyber PHA/HAZOP) 387 B.4 LOPA/ Semi-Quantitative SL Verification 405 Appendix C Example Cybersecurity Metrics 411 Appendix D Cybersecurity Sample Audit Question List 413 Appendix E Management System Review Examples 419 References 421 Index 437

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Book SynopsisFACTORIES OF THE FUTURE The book provides insight into various technologies adopted and to be adopted in the future by industries and measures the impact of these technologies on manufacturing performance and their sustainability. Businesses and manufacturers face a slew of demands beyond the usual issues of staying agile and surviving in a competitive landscape within a rapidly changing world. Factories of the Future deftly takes the reader through the continuous technology changes and looks ten years down the road at what manufacturing will mostly look like. The book is divided into two parts: Emerging technologies and advancements in existing technologies. Emerging technologies consist of Industry 4.0 and 5.0 themes, machine learning, intelligent machining, advanced maintenance, reliability, and green manufacturing. The advances of existing technologies consist of digital manufacturing, artificial intelligence in machine learning, Internet of Things, pTable of ContentsPreface xiii 1 Factories of the Future 1 Talwinder Singh and Davinder Singh 1.0 Introduction 2 1.1 Factory of the Future 3 1.1.1 Plant Structure 3 1.1.2 Plant Digitization 4 1.1.3 Plant Processes 4 1.1.4 Industry of the Future: A Fully Integrated Industry 5 1.2 Current Manufacturing Environment 6 1.3 Driving Technologies and Market Readiness 8 1.4 Connected Factory, Smart Factory, and Smart Manufacturing 11 1.4.1 Potential Benefits of a Connected Factory 13 1.5 Digital and Virtual Factory 13 1.5.1 Digital Factory 13 1.5.2 Virtual Factory 14 1.6 Advanced Manufacturing Technologies 14 1.6.1 Advantages of Advanced Manufacturing Technologies 16 1.7 Role of Factories of the Future (FoF) in Manufacturing Performance 17 1.8 Socio-Econo-Techno Justification of Factories of the Future 17 References 18 2 Industry 5.0 21 Talwinder Singh, Davinder Singh, Chandan Deep Singh and Kanwaljit Singh 2.1 Introduction 22 2.1.1 Industry 5.0 for Manufacturing 22 2.1.1.1 Industrial Revolutions 23 2.1.2 Real Personalization in Industry 5.0 25 2.1.3 Industry 5.0 for Human Workers 28 2.2 Individualized Human-Machine-Interaction 29 2.3 Industry 5.0 is Designed to Empower Humans, Not to Replace Them 31 2.4 Concerns in Industry 5.0 32 2.5 Humans Closer to the Design Process of Manufacturing 35 2.5.1 Enablers of Industry 5.0 36 2.6 Challenges and Enablers (Socio-Econo-Techno Justification) 37 2.6.1 Social Dimension 37 2.6.2 Governmental and Political Dimension 38 2.6.3 Interdisciplinarity 40 2.6.4 Economic Dimension 40 2.6.5 Scalability 41 2.7 Concluding Remarks 42 References 43 3 Machine Learning – A Survey 47 Navdeep Singh and Aanchal Goyal 3.1 Introduction 48 3.2 Machine Learning 49 3.2.1 Unsupervised Machine Learning 50 3.2.2 Variety of Unsupervised Learning 51 3.2.3 Supervised Machine Learning 52 3.2.4 Categories of Supervised Learning 54 3.3 Reinforcement Machine Learning 54 3.3.1 Applications of Reinforcement Learning 56 3.3.2 Dimensionality Reduction 57 3.4 Importance of Dimensionality Reduction in Machine Learning 58 3.4.1 Methods of Dimensionality Reduction 58 3.4.1.1 Principal Component Analysis (PCA) 58 3.4.1.2 Linear Discriminant Analysis (LDA) 59 3.4.1.3 Generalized Discriminant Analysis (GDA) 61 3.5 Distance Measures 61 3.6 Clustering 65 3.6.1 Algorithms in Clustering 67 3.6.2 Applications of Clustering 68 3.6.3 Iterative Distance-Based Clustering 69 3.7 Hierarchical Model 70 3.8 Density-Based Clustering 72 3.8.1 Dbscan 72 3.8.2 Optics 73 3.9 Role of Machine Learning in Factories of the Future 74 3.10 Identification of the Probable Customers 75 3.11 Conclusion 78 References 79 4 Understanding Neural Networks 83 Er. Lal Chand, Sikander Singh Cheema and Manpreet Kaur 4.1 Introduction 83 4.2 Components of Neural Networks 84 4.2.1 Neurons 85 4.2.2 Synapses and Weights 86 4.2.3 Bias 86 4.2.4 Architecture of Neural Networks 86 4.2.5 How Do Neural Networks Work? 87 4.2.6 Types of Neural Networks 88 4.2.6.1 Artificial Neural Network (ANN) 88 4.2.6.2 Recurrent Neural Network (RNN) 89 4.2.6.3 Convolutional Neural Network (CNN) 89 4.2.7 Learning Techniques in Neural Network 90 4.2.8 Applications of Neural Network 90 4.2.9 Advantages of Neural Networks 91 4.2.10 Disadvantages of Neural Network 91 4.2.11 Limitations of Neural Networks 92 4.3 Back-Propagation 92 4.3.1 Working of Back-Propagation 92 4.3.2 Types of Back-Propagation 93 4.3.2.1 Static Back-Propagation 93 4.3.2.2 Recurrent Back-Propagation 93 4.3.2.3 Advantages of Back-Propagation 94 4.3.2.4 Disadvantages of Back-Propagation 94 4.4 Activation Function (AF) 94 4.4.1 Sigmoid Active Function 94 4.4.1.1 Advantages 95 4.4.1.2 Disadvantages 95 4.4.2 RELU Activation Function 95 4.4.2.1 Advantages 96 4.4.2.2 Disadvantages 96 4.4.3 TANH Active Function 96 4.4.3.1 Advantages 97 4.4.3.2 Disadvantages 97 4.4.4 Linear Function 97 4.4.5 Advantages 98 4.4.6 Disadvantages 98 4.4.7 Softmax Function 98 4.4.8 Advantages 98 4.5 Comparison of Activation Functions 98 4.6 Machine Learning 99 4.6.1 Applications of Machine Learning 100 4.7 Conclusion 100 References 101 5 Intelligent Machining 103 Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak 5.1 Introduction 104 5.2 Requirements for the Developments of Intelligent Machining 104 5.3 Components of Intelligent Machining 105 5.3.1 Intelligent Sensors 106 5.3.1.1 Features of Intelligent Sensors 106 5.3.1.2 Functions of Intelligent Sensors 107 5.3.1.3 Data Acquisition and Management System to Process and Store Signals 111 5.3.2 Machine Learning and Knowledge Discovery Component 113 5.3.3 Database Knowledge Discovery 114 5.3.4 Programmable Logical Controller (PLC) 115 5.3.5 Role of Intelligent Machining for Implementation of Green Manufacturing 117 5.3.6 Information Integration via Knowledge Graphs 118 5.4 Conclusion 119 References 120 6 Advanced Maintenance and Reliability 121 Davinder Singh and Talwinder Singh 6.1 Introduction 121 6.2 Condition-Based Maintenance 122 6.3 Computerized Maintenance Management Systems (CMMS) 124 6.4 Preventive Maintenance (PM) 127 6.5 Predictive Maintenance (PdM) 128 6.6 Reliability Centered Maintenance (RCM) 129 6.6.1 RCM Principles 130 6.7 Condition Monitoring and Residual Life Prediction 131 6.8 Sustainability 133 6.8.1 Role of Sustainability in Manufacturing 134 6.9 Concluding Remarks 135 References 136 7 Digital Manufacturing 143 Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak 7.1 Introduction 144 7.2 Product Life Cycle and Transition 146 7.3 Digital Thread 148 7.4 Digital Manufacturing Security 150 7.5 Role of Digital Manufacturing in Future Factories 151 7.6 Digital Manufacturing and CNC Machining 152 7.6.1 Introduction to CNC Machining 152 7.6.2 Equipment’s Used in CNC Machining 153 7.6.3 Analyzing Digital Manufacturing Design Considerations 153 7.6.4 Finishing of Part After Machining 153 7.7 Additive Manufacturing 154 7.7.1 Objective of Additive Manufacturing 155 7.7.2 Design Consideration 155 7.8 Role of Digital Manufacturing for Implementation of Green Manufacturing in Future Industries 155 7.9 Conclusion 156 References 157 8 Artificial Intelligence in Machine Learning 161 Sikander Singh Cheema, Er. Lal Chand and Bhagwant Singh 8.1 Introduction 162 8.2 Case Studies 162 8.3 Advantages of A.I. in ml 164 8.4 Artificial Intelligence – Basics 166 8.4.1 History of A.I. 166 8.4.2 Limitations of Human Mind 166 8.4.3 Real Artificial Intelligence 166 8.4.4 Artificial Intelligence Subfields 167 8.4.5 The Positives of A.I. 167 8.4.6 Machine Learning 168 8.4.7 Machine Learning Models 168 8.4.8 Neural Networks 169 8.4.9 Constraints of Machine Learning 170 8.4.10 Different Kinds of Machine Learning 171 8.5 Application of Artificial Intelligence 171 8.5.1 Expert Systems 172 8.5.2 Natural Language Processing 172 8.5.3 Speech Recognition 172 8.5.4 Computer Vision 172 8.5.5 Robotics 172 8.6 Neural Networks (N.N.) Basics 173 8.6.1 Application of Neural Networks 173 8.6.2 Architecture of Neural Networks 173 8.6.3 Working of Artificial Neural Networks 175 8.7 Convolution Neural Networks 176 8.7.1 Working of Convolutional Neural Networks 176 8.7.2 Overview of CNN 181 8.7.3 Working of CNN 181 8.8 Image Classification 182 8.8.1 Concept of Image Classification 182 8.8.2 Type of Learning 182 8.8.3 Features of Image Classification 183 8.8.4 Examples of Image Classification 183 8.9 Text Classification 183 8.9.1 Text Classification Examples 183 8.9.2 Phases of Text Classification 184 8.9.3 Text Classification API 186 8.10 Recurrent Neural Network 186 8.10.1 Type of Recurrent Neural Network 187 8.11 Building Recurrent Neural Network 187 8.12 Long Short Term Memory Networks (LSTMs) 190 References 193 9 Internet of Things 195 Davinder Singh 9.1 Introduction 195 9.2 M2M and Web of Things 198 9.3 Wireless Networks 199 9.4 Service Oriented Architecture 203 9.5 Complexity of Networks 205 9.6 Wireless Sensor Networks 205 9.7 Cloud Computing 207 9.8 Cloud Simulators 211 9.9 Fog Computing 214 9.10 Applications of IoT 217 9.11 Research Gaps and Challenges in IoT 220 9.12 Concluding Remarks 223 References 224 10 Product Life Cycle 229 Harpreet Singh, Neetu Kaplas, Amant Sharma and Sahil Raj 10.1 Introduction 230 10.2 Product Lifecycle Management (PLM) 230 10.2.1 Why Product Lifecycle Management? 231 10.2.2 Biological Product Lifecycle Stages 231 10.2.3 An Example Related to Stages in Product Lifecycle Management 233 10.2.4 Advanced Stages in Product Lifecycle Management 234 10.2.5 Strategies of Product Lifecycle Management 235 10.3 High and Low-Level Skimming Strategies/Rapid or Slow Skimming Strategies 236 10.3.1 Considerations in High and Low-Level Pricing 236 10.3.2 Penetration Pricing Strategy 236 10.3.3 Example for Penetration Pricing Strategy 237 10.3.4 Considerations in Penetration Pricing 237 10.4 How Do Product Lifecycle Management Work? 240 10.5 Application Process of Product Lifecycle Management (plm) 241 10.6 Role of Unified Modelling Language (UML) 242 10.6.1 UML Activity Diagrams 243 10.7 Management of Product Information Throughout the Entire Product Lifecycle 244 10.8 PDM System in an Organization 245 10.8.1 Benefits of PDM 245 10.8.2 How Does the PDM Work? 245 10.8.3 The Services of Product Data Management 246 10.9 System Architecture 247 10.9.1 Process of System Architecture 248 10.10 Concepts of Model-Based System Engineering (MBSE) 250 10.10.1 Benefits of Model-Based System Engineering (mbse) 251 10.11 Challenges of Post-COVID 19 in Manufacturing Sector 251 10.12 Recent Updates in Product Life Cycle 252 10.13 Conclusion 253 References 254 11 Case Studies 257 Chandan Deep Singh and Harleen Kaur 11.1 Case Study in a Two-Wheeler Manufacturing Industry 258 11.1.1 Company Strategy 258 11.1.2 Initiatives Towards Technological Advancement 262 11.1.3 Management Initiatives 263 11.1.4 Sustainable Development Goals 265 11.1.5 Growth Framework with Customer Needs 269 11.1.6 Vision for the Future 270 11.2 Case Study in a Four-Wheeler Manufacturing Unit 271 11.2.1 Company Principles 271 11.2.2 Company Objectives 271 11.2.3 Company Strategy and Business Initiatives 272 11.2.4 Technology Initiatives 272 11.2.5 Management Initiatives 273 11.2.6 Quality 275 11.2.7 Sustainable Development Goals 276 11.2.8 Future Plan of Action 280 11.3 Conclusions 281 11.3.1 Limitations 282 11.3.2 Suggestions for Future Work 282 Index 285

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Book SynopsisDIGITAL CONVERGENCE in ANTENNA DESIGN The latest addition to this series presents high-quality original research contributions on analytical and practical models and ideas in the field of antennas, including a thorough look at RF techniques like antennas, RFID, and filters with special emphasis on real-time applications like e-health, RADAR, and mobile and satellite communications. This book is intended to disseminate recent trends in antenna designs for real-time applications that leverage digital convergence. The book intends to report the latest research findings, as well as the state-of-the-art RF techniques related to antennas, RFID, filters, etc., with special emphasis on real-time applications like e-health, RADAR, and mobile and satellite communications. The book can be used as a reference for researchers who want to explore the convergence of AI/ML/DL, big data, and IoT in the areas of antenna and advanced communication technologies for real-tim

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Book SynopsisROLE OF MICROBES IN INDUSTRIAL PRODUCTS AND PROCESSES The book covers recent breakthroughs and highlights the major role microbes play in industrial products and processes. With the advent of industrial biotechnology, microbes became popular as cell factories, and with the recent advancements in recombinant DNA technology, the application of microorganisms in various sectors has increased enormously for the development of various processes and products. Role of Microbes in Industrial Products and Processes covers recent breakthroughs and highlights the major role microbes play in industrial products and processes. It mainly focuses on the bio-refinery concept where bio-energy production and wastewater treatment are done simultaneously using micro-algae. Additionally, this book describes the role of microbes involved in the production of various enzymes, organic acids, and bio-polymers. It also provides detailed insight on modeling and simulation of bioprocess for the production of suga

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Book SynopsisDistillation has historically been the main method for separating mixtures in the chemical process industry. However, despite the flexibility and widespread use of distillation processes, they still remain extremely energy inefficient.Trade Review“In conclusion, this book will be of most interest to chemical engineers working in the field of process intensification and distillation of petrochemicals and related materials.” (Organic Process Research & Development Journal, 26 July 2013) Table of ContentsPreface xiii Acknowledgements xv 1 Basic Concepts in Distillation 1 1.1 Introduction 1 1.2 Physical Property Methods 2 1.3 Vapor Pressure 6 1.4 Vapor–Liquid Equilibrium and VLE Non-ideality 8 1.5 Relative Volatility 13 1.6 Bubble Point Calculations 14 1.7 Ternary Diagrams and Residue Curve Maps 16 1.8 Analysis of Distillation Columns 24 1.9 Concluding Remarks 34 References 35 2 Design, Control and Economics of Distillation 37 2.1 Introduction 37 2.2 Design Principles 38 2.3 Basics of Distillation Control 44 2.4 Economic Evaluation 55 2.5 Concluding Remarks 63 References 64 3 Dividing-Wall Column 67 3.1 Introduction 67 3.2 DWC Configurations 70 3.3 Design of DWCs 75 3.4 Modeling of a DWC 83 3.5 DWC Equipment 87 3.6 Case Study: Separation of Aromatics 97 3.7 Concluding Remarks 103 References 107 4 Optimal Operation and Control of DWC 111 4.1 Introduction 111 4.2 Degrees of Freedom Analysis 112 4.3 Optimal Operation and Vmin Diagram 114 4.4 Overview of DWC Control Structures 117 4.5 Control Guidelines and Rules 128 4.6 Case Study: Pentane–Hexane–Heptane Separation 129 4.7 Case Study: Energy Efficient Control of a BTX DWC 132 4.8 Concluding Remarks 148 References 149 5 Advanced Control Strategies for DWC 153 5.1 Introduction 153 5.2 Overview of Previous Work 154 5.3 Dynamic Model of a DWC 156 5.4 Conventional versus Advanced Control Strategies 163 5.5 Energy Efficient Control Strategies 171 5.6 Concluding Remarks 180 Notation 181 References 183 6 Applications of Dividing-Wall Columns 187 6.1 Introduction 187 6.2 Separation of Ternary and Multicomponent Mixtures 188 6.3 Reactive Dividing-Wall Column 195 6.4 Azeotropic Dividing-Wall Column 198 6.5 Extractive Dividing-Wall Column 199 6.6 Revamping of Conventional Columns to DWC 203 6.7 Case Study: Dimethyl Ether Synthesis by R-DWC 205 6.8 Case Study: Bioethanol Dehydration by A-DWC and E-DWC 212 6.9 Concluding Remarks 223 References 223 7 Heat Pump Assisted Distillation 229 7.1 Introduction 229 7.2 Working Principle 231 7.3 Vapor (Re)compression 232 7.4 Absorption–Resorption Heat Pumps 234 7.5 Thermo-acoustic Heat Pump 236 7.6 Other Heat Pumps 240 7.7 Heat-Integrated Distillation Column 244 7.8 Technology Selection Scheme 245 7.9 Concluding Remarks 265 References 265 8 Heat-Integrated Distillation Column 271 8.1 Introduction 271 8.2 Working Principle 273 8.3 Thermodynamic Analysis 277 8.4 Potential Energy Savings 280 8.5 Design and Construction Options 282 8.6 Modeling and Simulation 295 8.7 Process Dynamics, Control, and Operation 297 8.8 Applications of HIDiC 300 8.9 Concluding Remarks 304 References 305 9 Cyclic Distillation 311 9.1 Introduction 311 9.2 Overview of Cyclic Distillation Processes 313 9.3 Process Description 316 9.4 Mathematical and Hydrodynamic Model 319 9.5 Modeling and Design of Cyclic Distillation 327 9.6 Control of Cyclic Distillation 335 9.7 Cyclic Distillation Case Studies 338 9.8 Concluding Remarks 347 References 349 10 Reactive Distillation 353 10.1 Introduction 353 10.2 Principles of Reactive Distillation 354 10.3 Design, Control and Applications 357 10.4 Modeling Reactive Distillation 362 10.5 Feasibility and Technical Evaluation 364 10.6 Case Study: Advanced Control of a Reactive Distillation Column 371 10.7 Case Study: Biodiesel Production by Heat-Integrated RD 378 10.8 Case Study: Fatty Esters Synthesis by Dual RD 383 10.9 Concluding Remarks 387 References 388 Index 393

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Book SynopsisWith resources at a premium, and ecological concerns paramount, the need for clean, efficient and low-cost processes is one of the most critical challenges facing chemical engineers. The ability to control these processes, optimizing one, two or several variables has the potential to make more substantial savings in time, money and resources than any other single factor. Building on the success of the previous editions, this new third edition of A Real-Time Approach to Process Control employs both real industry practice and process control education without the use of complex or highly mathematical techniques, providing a more practical and applied approach. Updated throughout, this edition: Includes a brand new chapter on Model predictive Control (MPC) Now includes wireless and web-based technologies Covers bio-related systems Details the new multivariable control measure developed by the authors Includes PowTable of ContentsAuthor Biographies xi Foreword and Endorsements xiii Preface xv Acknowledgements xvii 1 A Brief History of Process Control and Process Simulation 1 1.1 Process Control 1 1.2 Process Simulation 5 References 11 2 Process Control Hardware Fundamentals 15 2.1 Control System Components 15 2.2 Primary Elements 16 2.3 Final Control Elements 33 References 53 3 Fundamentals of Single-Input/Single-Output Systems 55 3.1 Open Loop Control 55 3.2 Disturbances 56 3.3 Feedback Control – Overview 57 3.4 Feedback Control – A Closer Look 60 3.5 Process Attributes – Capacitance and Dead Time 66 3.6 Process Dynamic Response 74 3.7 Process Modelling and Simulation 76 References 93 4 Basic Control Modes 95 4.1 On–Off Control 95 4.2 Proportional (P-Only) Control 97 4.3 Integral (I-Only) Control 102 4.4 Proportional Plus Integral (PI) Control 105 4.5 Derivative Action 107 4.6 Proportional Plus Derivative (PD) Controller 108 4.7 Proportional Integral Derivative (PID) Control 111 4.8 Digital Electronic Controller Forms 112 4.9 Choosing the Correct Controller 112 4.10 Controller Hardware 114 References 117 5 Tuning Feedback Controllers 119 5.1 Quality of Control and Optimization 119 5.2 Tuning Methods 123 References 132 6 Advanced Topics in Classical Automatic Control 133 6.1 Cascade Control 133 6.2 Feedforward Control 137 6.3 Ratio Control 140 6.4 Override Control (Auto Selectors) 142 6.5 Split Range Control 147 References 149 7 Common Control Loops 151 7.1 Flow Loops 151 7.2 Liquid Pressure Loops 153 7.3 Liquid Level Control 155 7.4 Gas Pressure Loops 165 7.5 Temperature Control Loops 166 7.6 Pump Control 172 7.7 Compressor Control 172 7.8 Boiler Control 179 References 182 8 Distillation Column Control 185 8.1 Basic Terms 185 8.2 Steady-State and Dynamic Degrees of Freedom 186 8.3 Control System Objectives and Design Considerations 188 8.4 Methodology for Selection of a Controller Structure 190 8.5 Level, Pressure, Temperature and Composition Control 192 8.6 Optimizing Control 199 Section Sidestream 199 8.7 Distillation Control Scheme Design Using Steady-State Models 204 8.8 Distillation Control Scheme Design Using Dynamic Models 212 References 213 9 Using Steady-State Methods in a Multi-loop Control Scheme 215 9.1 Variable Pairing 215 9.2 The Relative Gain Array 216 9.3 Niederlinski Index 220 9.4 Decoupling Control Loops 220 9.5 Tuning the Controllers for Multi-loop Systems 222 9.6 Practical Examples 222 9.7 Summary 232 References 232 10 Plant-Wide Control 233 10.1 Short-Term versus Long-Term Control Focus 233 10.2 Cascaded Units 235 10.3 Recycle Streams 236 10.4 General Considerations for Plant-Wide Control 241 References 242 11 Advanced Process Control 245 11.1 Advanced Process Control 245 11.2 Model Predictive Control 246 11.3 Dynamic Matrix Control 249 11.4 General Considerations for Model Predictive Control Implementation 253 References 254 Appendix A P&ID Symbols 257 Appendix B Glossary of Terms 261 Appendix C New Capabilities with Control Technology Hardware and Software 267 Workshop 1 Learning through Doing 279 Workshop 2 Feedback Control Loop Concepts 283 Workshop 3 Process Capacity and Dead Time 289 Workshop 4 Feedback Control 295 Workshop 5 Controller Tuning for Capacity and Dead Time Processes 303 Workshop 6 Topics in Advanced Control 311 Workshop 7 Distillation Control 321 Workshop 8 Plant Operability and Controllability 333 Index

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Book SynopsisINDUSTRIAL VALVES Improve the design and safety of your industrial valves with this comprehensive guide Industrial valves are used to regulate the flow of liquids, gases, or slurries. They are fundamental to multiple industries, including marine shipping, in which valves regulate power supply, wastewater, water for fire-fighting, and other shipboard essentials. They are also critical to the oil and gas industry, where valves are used to control the flow of oil or gas out of deposits, direct the crude oil refining process, protect key areas and equipment from spillage and overflow, and more. Without the safety and regulating power provided by industrial valves these industries could not proceed. This book provides a thorough introduction to the modeling and calculation of key challenges related to valve design, manufacturing, and operation. It focuses particularly on solving problems of material failure due to corrosion and cavitation, allowing readers to construcTable of Contents1 Flow Capacity 1 1.1 Introduction 1 1.2 Flow Coefficient Chart and Flow Curve 8 1.3 Rangeability and Turndown 12 1.4 Valve Authority 14 1.5 Valve Gain 15 Questions and Answers 16 Further Reading 20 2 Valve Sizing 22 2.1 Introduction 22 2.2 Isolation Valve Sizing 22 2.3 Nonreturn (Check) Valve Sizing 26 2.4 Control Valve Sizing 34 2.4.1 Control Valve Sizing for Liquids 34 2.4.1.1 Specify the Variables Required to Size the Valve 35 2.4.1.2 Determine the Equation Constant (N) 37 2.4.1.3 Determine Piping Geometry Factor (FP) 37 2.4.1.4 Determine the Maximum Flow Rate (qmax) and Maximum Pressure Drop (ΔPmax) 39 2.4.1.5 Solve for Flow Coefficient 44 2.4.1.6 Select the Correct Valve Size 44 2.4.2 Control Valve Sizing for Gas and Steam 47 2.4.2.1 Specify the Variables Required to Size the Valve 47 2.4.2.2 Determine the Equation Constant (N) 48 2.4.2.3 Determine Piping Geometry Factor (FP) 48 2.4.2.4 Determine the Expansion Factor (Y) 48 2.4.2.5 Solve for the Required Flow Coefficient (Cv) 50 2.5 Safety Relief Valve Sizing 56 2.5.1 Sizing for Gas or Vapor Relief 59 2.5.1.1 Critical Flow 59 2.5.1.2 Subcritical Flow 73 2.5.2 Sizing for Steam Relief 75 2.5.3 Sizing for Liquid Relief 79 2.5.3.1 Sizing for Liquid Relief with Capacity Certification 79 2.5.3.2 Sizing for Liquid Relief Without Capacity Certification 84 2.5.4 Sizing for Two-Phase Liquid/Vapor Relief 85 2.5.4.1 Sizing for Saturated Liquid and Saturated Vapor, Liquid Flashes 88 2.5.4.2 Sizing for Subcooled at the Pressure Relief Valve Inlet 91 2.5.5 Sizing for Fire Case and Hydraulic Expansion 93 2.5.5.1 Hydraulic Expansion (Thermal Expansion) 95 2.5.5.2 Sizing Safety Valve for the Fire Case 96 Questions and Answers 103 Further Reading 110 3 Cavitation and Flashing 112 3.1 Introduction 112 3.2 Cavitation 112 3.2.1 What is Cavitation? 112 3.2.2 Cavitation Essential Parameters 113 3.2.3 Cavitation Analysis 115 3.3 Flashing 116 Questions and Answers 118 Further Reading 123 4 Wall Thickness 125 4.1 Introduction 125 4.2 ASME B16.34 Minimum Wall Thickness Calculation 125 4.2.1 Conservation Approach (Mandatory Appendix A) 125 4.2.2 Nonconservation Method 129 4.2.3 ASME Sec. VIII Div. 02 Wall Thickness Calculation 134 4.3 Wafer Design Thickness Validation 136 Questions and Answers 142 Further Reading 147 5 Material and Corrosion 149 5.1 Introduction 149 5.2 Carbon Dioxide Corrosion 150 5.2.1 Corrosion Mechanism 150 5.2.2 Corrosion Mitigation 151 5.2.3 Corrosion Rate Calculation 152 5.2.3.1 Basic CO2 Corrosion Rate 152 5.2.3.2 Corrective CO2 Corrosion Rate 154 5.2.3.3 Final CO2 Corrosion Rate 161 5.3 Pitting Corrosion 162 5.4 Carbon Equivalent 165 5.5 Hydrogen-Induced Stress Cracking (HISC) Corrosion 167 5.5.1 HISC and Vulnerable Materials 168 5.5.2 HISC and Stress 168 5.5.3 HISC and Cathodic Protection 168 5.5.4 HISC and DNV Standard 169 Questions and Answers 177 Further Reading 184 6 Noise 185 6.1 Introduction to Sound 185 6.2 Introduction to Noise 186 6.3 Noise in Industrial Valves 189 6.3.1 Mechanical Noise and Vibration 190 6.3.2 Fluid Noise 190 6.3.2.1 Aerodynamic Noise 191 6.3.2.2 Hydrodynamic Noise 191 6.3.3 Noise Control Strategies 191 6.4 Noise Calculations for Pipes and Valves 192 6.4.1 Acoustic Fatigue Analysis 192 6.4.1.1 Sound Power Level Calculations 193 6.4.1.2 Mach Number 198 6.4.2 Noise in Control Valves 203 6.4.2.1 Aerodynamic Noise in Control Valves 203 6.4.2.2 Hydrodynamic Noise in Control Valves 208 6.4.3 Noise in Pressure Safety or Relief Valves 215 6.4.3.1 Calculation of Noise Emission According to ISO 4126-9 216 6.4.3.2 Calculation of Noise Emission According to API 521 218 6.4.3.3 Calculation of Noise Emission According to VDI 2713 221 Questions and Answers 222 Further Reading 231 7 Water Hammering 233 7.1 Introduction 233 7.2 Water Hammering and Pressure Loss in Check Valves 233 7.3 Water Hammering Calculations 243 Questions and Answers 249 Further Reading 256 8 Safety Valves 258 8.1 Introduction 258 8.2 Safety Valve Parts 259 8.3 Safety Valve Design and Operation 259 8.3.1 Design and Operation Parameters 259 8.3.1.1 Overpressure Criteria 277 8.3.2 Principle of Operation 278 8.3.3 Safety Valve Reaction Forces 282 8.3.4 Safety Valve Capacity Conversion 294 Questions and Answers 296 Further Reading 302 9 Safety and Reliability 304 9.1 Introduction 304 9.2 Safety Standards 305 9.3 Risk Analysis 308 9.4 Basic Safety and Reliability Concepts 312 9.4.1 System Incidents and Failures 312 9.4.1.1 Failure Rate 313 9.4.1.2 Repair Rate 317 9.4.1.3 Mean Time to Failure (MTTF) 317 9.4.1.4 Mean Time Between Failure (MTBF) 318 9.4.1.5 Mean Time to Repair and Recovery (MTTR) 319 9.4.1.6 Mean Time to Detection (MTTD) 319 9.4.2 Reliability and Unreliability 319 9.4.3 Availability and Unavailability 331 9.5 Safety Integrity Level (SIL) Calculations 336 9.5.1 SIL 336 9.5.2 Probability of Failure on Demand (PFD) 338 9.5.3 Mean Downtime 339 9.5.4 Diagnostic Coverage 342 9.5.5 Safe Failure Fraction (SFF) 342 9.6 Condition Monitoring (ValveWatch) 347 Questions and Answers 348 Further Reading 354 10 Valve Operation 357 10.1 Introduction 357 10.2 Valve Torque 358 10.3 Stem Design 363 10.3.1 MAST Calculations 363 10.3.2 Buckling Prevention 369 10.3.3 Torsional Deflection Prevention 374 10.3.4 MAST Limitation for Quarter-Turn Cryogenic Valves 376 Questions and Answers 378 Further Reading 384 11 Miscellaneous 385 11.1 Introduction 385 11.2 Joint Efficiency 386 11.2.1 Weld Joint Efficiency 386 11.2.2 Bolted Joint Efficiency 388 11.2.2.1 Bolted Bonnet or Cover Joints 388 11.2.2.2 Bolted Body Joints 392 11.2.3 Threaded Joint Efficiency 394 11.2.3.1 Threaded Bonnet or Cover Joints 394 11.2.3.2 Threaded Body Joints 395 11.3 Stem Sealing 395 Questions and Answers 399 Further Reading 405 Index 407

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Book SynopsisVolume 4: Optical Sensors covers various approaches used for modelling and simulation of different types optical sensors such as fibre optic, surface Plasmon resonance, Fabry-Perot interferometer, transmittance in the mid-infrared region, luminescence based devices, etc. Approaches used for design and optimization of optical systems aimed for remote gas sensing and gas analysis chamber for NDIR spectral range are discussed as well. The description of multiscale atomistic simulation of hierarchical nanostructured materials for optical chemical sensing is also included in present volume. This 5 volume reference work covering simulation and modelling will serve as the perfect complement to Momentum Pressâs 6 volume reference works Chemical Sensors: Fundamentals of Sensing Materials and Chemical Sensors: Comprehensive Sensor Technologies, which present detailed information related to materials, technologies, construction and application of various devices for chemical sensing.

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Book SynopsisThe Mahalanobis-Taguchi data handling and pattern recognition system is widely established - built and extended from the original quality control precepts of Genichi Taguchi. But the MT system is not always well understood. This new book makes the system much more vivid and concrete with real-life applications in a wide variety of disciplines from industry to general commerce. The book offers a clear computational method to show the user how to actually apply the system to real manufacturing control problems. With the renowned international industry background of the three authors and their historic ties to Genichi Taguchi, this book will bring a unique insight into how to get the most benefits from the MT System. The book offers: An overview of pattern recognition issues and the precepts of the MT system Explains the merits of the MT System and its computational methods Shows how to handle data with the MT System and extract useful information Provides a useful comparison of the advantages and disadvantages between traditional Artificial Intelligence systems and the MT system Provides case study examples of MT Systems applications

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Book SynopsisEngineers and technologists often operate from a worldview of 'ones and zeros.' The mission of this book is to interject the colorful world of creative thinking to help engineers and technologists learn to think and work differently. Thus, 'idea engineering' becomes the driving force, transforming engineers and technologists into innovators and entrepreneurs, using case studies and anecdotes from first-hand experience. The material in this book is organized to take the reader through basic concepts and techniques of creative thinking and innovation, to better solve engineering and technological challenges. It provides an overall understanding of who, what, why, when, and how 'idea engineering' can transform an individual and a company to formulate and apply the best possibilities.

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Book SynopsisThe Basics of Polymers is written exclusively about chemical methods of polymer testing aimed at producing a high degree of manufacturing and quality control of polymer products. Polymer testing has assumed importance for industries dependent on polymers and additives as key product components. The text is intended to serve as a handbook for students, engineers, and people involved in polymer synthesis and laboratory work. This book provides information on identification and characterization of polymers by chemical methods. Specifically aimed at graduate-level students, its style of presentation is practical, making it easier to grasp. The author hopes this book will encourage and foster continuing method development and application of chemical methods for characterizing polymers. Education and training of people being of paramount importance, it is also valuable to all educators/processors as a tremendous resource that answers commonly asked questions.

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Book SynopsisOur world changes faster today than at any time in the history of mankind. Organizations, like living breathing organisms, must learn to adapt to changes in the environment in which each operates. It is generally held today, by those who study organizations, that those who fail to adapt to seemingly unending change are certainly doomed but those able to adapt to constant change tend to thrive. The purpose of this book is to describe the leadership required to successfully implement continuous process improvement in organizations. The author begins the journey with a discussion of organizational culture as he sets out to describe how leaders develop a culture where continuous improvement can thrive. The challenges of organizational change faced by all leaders who strive to take advantage of the benefits of continuous process improvement is discussed, as well as what leaders must do to make change stick. The goal is to provide a description of the leadership necessary to make continuous process improvement a reality in any organization.

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Book SynopsisSustainable production automation, as an effective way to enable and expedite transitions to sustainability and enhance resource utilizations, attracts substantial efforts from researchers in both academy and industry. This book presents the recent development of innovative algorithms, models, heuristics, hardware and software in broad areas of sustainable production systems. It focuses on design, analysis and management of the processes involved in the product life cycle (from design to delivery to return) to have the minimal negative impacts on society (including environmental, economic and social). The contributors are experts from both universities and industrial research centers.

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Book SynopsisProcess control is essential in modern manufacturing. The control system is the eyes, ears, and nervous system of the plant. It senses, decides, and directs the activities of the pumps, valves, motors, and other equipment. The control system handles many routine tasks, freeing up the operator to oversee the operation and handle new situations that arise. Without process control, it would be nearly impossible to efficiently produce commodities like pulp and paper, gasoline, plastic, and pharmaceuticals.Most people learn process control through hands-on plant experience, accompanied by a healthy dose of self-study. This is because textbooks generally address the mathematics of process dynamics and control, but often miss the practical aspects. This easy-to-read book fills the gap by focusing on practical real-world knowledge of process control systems, providing clear and concise examples, and providing practical advice for handling day-to-day maintenance and documentation. The author begins by discussing control terminology, principles, and applications, the information one needs to form a basic understanding of process control. He then explains the differences between discrete, continuous, and batch control, as well as the different control systems, programming languages, and documentation needed for each. To complete the foundation, the author addresses the management of control systems including discussions about maintenance, change management, communications, and documentation. Finally, one chapter introduces advanced control topics such as advanced regulatory control, multivariable control, and neural networks.Whether you are a student of process control, a technician or engineer expanding their skills, or someone in operations, maintenance, sales, support, or management who wants to develop a basic understanding of process control, this book is for you.

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Book SynopsisThe objective of the book is to provide all the elements to evaluate the performance of production availability and reliability of a system, to integrate them and to manage them in its life cycle. By the examples provided (case studies) the main target audience is that of the petroleum industries (where I spent most of my professional years). Although the greatest rigor is applied in the presentation, and justification, concepts, methods and data this book is geared towards the user.Table of ContentsPreface xv Chapter 1. Basic Concepts 1 1.1. Introduction 1 1.2. Definition of terms 1 1.2.1. Risk 1 1.2.2. Time definitions 2 1.2.3. Failures and repairs 4 1.2.4. IEC 61508 terms 8 1.3. Definition of parameters 10 1.3.1. Reliability 10 1.3.2. Maintainability 12 1.3.3. Availability and production availability 12 1.3.4. Dependability 13 1.3.5. Definitions used by maintenance engineers 13 1.3.6. Definitions used in the refinery industry 14 1.4. The exponential law/the constant failure rate 14 1.4.1. Reliability 14 1.4.2. Validity 15 1.4.3. Oil and gas industry 16 1.5. The bathtub curve 16 1.5.1. Meaning 16 1.5.2. Useful life and mission life 18 1.5.3. Validity 18 1.5.4. Oil and gas industry 18 Chapter 2. Mathematics for Reliability 21 2.1. Introduction 21 2.2. Basis of probability and statistics 22 2.2.1. Boolean algebra 22 2.2.2. Probability relations 22 2.2.3. Probability distributions 24 2.2.4. Characteristics of probability distributions 24 2.2.5. Families and conjugates 26 2.3. Formulae and theorems 27 2.3.1. Combinatorial analysis 27 2.3.2. Central limit theorem 28 2.3.3. Chebyshev’s inequality 28 2.3.4. Laws of large numbers 28 2.3.5. Supporting functions and distributions 29 2.3.6. Bayes’ theorem 30 2.4. Useful discrete probability distributions 32 2.4.1. Binomial distribution 33 2.4.2. Poisson distribution. 33 2.5. Useful continuous probability distributions 35 2.5.1. Exponential distribution 35 2.5.2. Uniform distribution 36 2.5.3. Triangular distribution 37 2.5.4. Normal distribution 38 2.5.5. Log-normal distribution 40 2.5.6. Weibull distribution 43 2.5.7. Gamma distribution 44 2.5.8. Beta distribution 45 2.5.9. Chi-squared distribution 46 2.5.10. Fisher-Snedecor distribution 46 2.6. Statistical estimates 47 2.6.1. Estimates 47 2.6.2. Calculation of point estimate 47 2.6.3. Calculation of confidence interval 50 2.6.4. Heterogeneous samples 52 2.6.5. Implementation 53 2.7. Fitting of failure distribution 53 2.7.1. Principle 53 2.7.2. Median rank method 54 2.7.3. Implementation 55 2.8. Hypothesis testing 57 2.8.1. Principle 57 2.8.2. Existing tests. 58 2.8.3. Implementation 58 2.9. Bayesian reliability 60 2.9.1. Definition 60 2.9.2. Use of Bayes’ theorem 61 2.9.3. Bayesian inference 61 2.9.4. Selection of the prior probability distribution 62 2.9.5. Determination of the posterior probability distribution 62 2.9.6. Bayesian credibility interval 64 2.10. Extreme value probability distributions 65 2.10.1. Meaning. 65 2.10.2. The three extreme value probability distributions 65 2.10.3. Use in the industry 66 Chapter 3. Assessment of Standard Systems 67 3.1. Introduction 67 3.2. Single item 67 3.2.1. Availability 68 3.2.2. Number of failures 69 3.3. System reliability 70 3.3.1. Series systems 70 3.3.2. Parallel systems 72 3.4. Specific architectures 73 3.4.1. Method of analysis 73 3.4.2. Redundant item system 74 3.5. On-guard items 76 3.5.1. Unrevealed failures 76 3.5.2. Full formula 77 3.5.3. Optimum proof test duration 79 Chapter 4. Classic Methods 81 4.1. Introduction 81 4.2. Failure Mode and Effects Analysis 81 4.2.1. Conventional Failure Mode and Effects Analysis/Failure Mode,Effects and Criticality Analysis 81 4.2.2. Functional/hardware FMEA 84 4.2.3. Case study 84 4.3. Fault trees 89 4.3.1. Conventional fault trees 89 4.3.2. Fault tree extensions 93 4.3.3. Facilities provided by software packages 94 4.3.4. Case study 94 4.4. Reliability block diagrams 98 4.4.1. Conventional RBDs 98 4.4.2. RBD extension 102 4.4.3. Facilities provided by software packages 103 4.4.4. Case study 103 4.5. Monte Carlo method 104 4.5.1. Principle 104 4.5.2. Use for production availability and reliability 106 4.5.3. How many runs are enough? 107 Chapter 5. Petri Net Method 109 5.1. Introduction 109 5.2. Petri nets 110 5.2.1. Definition 110 5.2.2. Mathematical properties 111 5.2.3. Petri net construction 112 5.2.4. GRAFCET 117 5.3. IEC 62551 extensions 117 5.3.1. Extensions to structure 117 5.3.2. Modified execution rules 120 5.4. Additional extensions 121 5.4.1. Extensions to structure 121 5.4.2. Modified execution rules 122 5.5. Facilities provided by software packages 123 5.5.1. Additional extensions to structure 123 5.5.2. Modified execution rules 123 5.5.3. Petri net processing 123 5.5.4. Results 123 5.6. Petri net construction 124 5.6.1. Petri net modeling 124 5.6.2. Minimizing the risk of error input 124 5.6.3. Petri net checking 124 5.6.4. Petri net validation 125 5.7. Case study 125 5.7.1. System description 125 5.7.2. Petri net model 126 Chapter 6. Sources of Reliability Data 133 6.1. Introduction 133 6.2. The OREDA project 133 6.2.1. History 133 6.2.2. Project management and organization 135 6.2.3. Description of OREDA 2015 handbooks 135 6.2.4. Use of the data tables 137 6.2.5. Use of the additional tables 141 6.2.6. Reliability database and data analysis software 143 6.2.7. Data collection software 144 6.3. The PDS handbook 144 6.3.1. History 144 6.3.2. Description of the handbook 145 6.3.3. Use of the handbook 145 6.4. Reliability Analysis Center/Reliability Information Analysis Center publications 145 6.4.1. History 145 6.4.2. Non-electronic Part Reliability Data handbook 146 6.4.3. FMD 146 6.4.4. NONOP 146 6.4.5. Use of the publications 146 6.5. Other publications 147 6.5.1. EXIDA handbooks 147 6.5.2. Electrical items 147 6.5.3. Pipelines 148 6.5.4. Flexibles 149 6.5.5. Miscellaneous 149 6.6. Missing information 150 Chapter 7. Use of Reliability Test and Field Data 151 7.1. Introduction 151 7.2. Reliability test data 151 7.2.1. Principle 151 7.2.2. Test organization 152 7.2.3. Assessment of failure rate 152 7.3. Field data 154 7.3.1. Principle 154 7.3.2. Data collection organization 155 7.3.3. Assessment of failure rate 155 7.3.4. Assessment of probability to fail upon demand 156 7.3.5. Assessment of MRT 156 7.3.6. Case study 156 7.4. Accelerated tests 157 7.4.1. Principle 157 7.4.2. Example 158 7.4.3. Highly accelerated tests 159 7.5. Reliability growth 159 7.5.1. Principle 159 7.5.2. Main models 159 Chapter 8. Use of Expert Judgment. 163 8.1. Introduction 163 8.2. Basis 164 8.2.1. Definitions 164 8.2.2. Protocol for expert elicitation 164 8.2.3. Role of the facilitator 165 8.3. Characteristics of the experts 166 8.3.1. Definition 166 8.3.2. Selection 166 8.3.3. Biases 167 8.3.4. Expert weighting 168 8.3.5. Expert dependence 169 8.3.6. Aggregation of judgments 169 8.4. Use of questionnaires 169 8.4.1. Conditions of use 169 8.4.2. The Delphi method 170 8.4.3. Case study 171 8.5. Use of interactive group 173 8.5.1. Number of experts 173 8.5.2. Procedure. 173 8.6. Use of individual interviews 174 8.6.1. Conditions of use 174 8.6.2. Case study 174 8.7. Bayesian aggregation of judgment 175 8.7.1. Form of information provided by experts 175 8.7.2. Assessment of failure rate (or MTBF) 176 8.7.3. Assessment of probability of failure upon demand 177 8.8. Validity of expert judgment 177 Chapter 9. Supporting Topics 179 9.1. Introduction 179 9.2. Common cause failures 179 9.2.1. Introduction 179 9.2.2. Definition 180 9.2.3. Defenses against CCF 181 9.2.4. CCF modeling with the beta-factor method 182 9.2.5. CCF modeling with the shock method 185 9.2.6. Extension of the beta-factor model: the PDS method 188 9.2.7. Field data 189 9.2.8. Impact of CCF on system reliability 190 9.2.9. Impact of testing policy on CCF 191 9.2.10. Impact of CCF on system production availability 194 9.2.11. Benchmark on CCF assessment 194 9.3. Mechanical reliability 195 9.3.1. Characteristics 195 9.3.2. Stress-strength interference 195 9.3.3. Empirical reliability relationships 197 9.3.4. Comparison with system (constant failure rate) approach 199 9.4. Reliability of electronic items 199 9.4.1. Characteristics 199 9.4.2. MIL-HDBK-217 200 9.4.3. UTE-C-80811 201 9.4.4. Other reliability data books 201 9.4.5. EPRD 203 9.4.6. Effect of dormancy period 203 9.4.7. Common cause failures 203 9.4.8. Comparison of previsions 204 9.4.9. Use in the oil and gas industry 205 9.5. Human reliability 205 9.5.1. Human factors 205 9.5.2. Human reliability in the nuclear industry 205 9.5.3. Evaluation of HRA techniques 206 9.5.4. Human reliability in the oil and gas industry 206 Chapter 10. System Reliability Assessment 209 10.1. Introduction 209 10.2. Definition of reliability target 209 10.2.1. Absolute reliability target 209 10.2.2. Risk target 210 10.3. Methodology of system reliability study 211 10.3.1. Overall description 211 10.3.2. Step 1: system analysis 212 10.3.3. Step 2: qualitative analysis. 212 10.3.4. Step 3: quantitative data selection 212 10.3.5. Step 4: system reliability modeling 214 10.3.6. Step 5: synthesis 214 10.4. SIL studies 214 10.4.1. Introduction 214 10.4.2. SIL assignment 214 10.4.3. SIL demonstration 217 10.5. Description of the case study 217 10.5.1. Origin of the risk 217 10.5.2. Description of the standard SIF 219 10.5.3. Risk assessment 219 10.6. System analysis 220 10.6.1. Description of HIPS functioning 220 10.7. Qualitative analysis 221 10.7.1. FMEA 221 10.7.2. CCF analysis 223 10.8. Quantitative data selection 225 10.8.1. Selection of reliability data 225 10.8.2. Collection of proof test data 225 10.8.3. CCF quantification 226 10.9. System reliability modeling 226 10.9.1. Building of system reliability model 226 10.9.2. System reliability calculation 226 10.10. Synthesis 232 10.10.1. Conclusions 232 10.10.2. Recommendations 233 10.11. Validity of system reliability assessments 234 10.11.1. Reports 234 10.11.2. Conclusions 234 Chapter 11. Production Availability Assessment 235 11.1. Introduction 235 11.2. Definition of production availability target 235 11.2.1. Absolute production availability target 235 11.2.2. Economic target 235 11.3. Methodology 236 11.3.1. Events considered in production availability assessments 236 11.3.2. Overall description 236 11.3.3. Step 1: system analysis 238 11.3.4. Step 2: quantitative data selection 238 11.3.5. Step 3: production availability assessment 238 11.3.6. Step 4: synthesis 238 11.4. System analysis 239 11.4.1. Determination of system running modes 239 11.4.2. Item failure analysis 242 11.5. Quantitative data selection 244 11.5.1. Selection of reliability data 244 11.5.2. Collection of operational data 245 11.6. Production availability assessment 246 11.6.1. Building of production availability model 246 11.6.2. Production availability calculations 246 11.7. Synthesis 248 11.7.1. Main results 248 11.7.2. Additional economic parameters 249 11.7.3. Flared gas 251 11.7.4. Other results 253 11.7.5. Recommendations 256 11.8. Uncertainty on the reliability parameters 256 11.9. Validity of production availability assessments 257 Chapter 12. Management of Production Availability and Reliability 259 12.1. Introduction 259 12.2. Principles of dependability management 260 12.2.1. Dependability property management 260 12.2.2. Phasing of the management 260 12.2.3. Lifecycle costing and dependability 261 12.3. Technical specifications 262 12.3.1. Contents. 262 12.3.2. Reliability specification 262 12.3.3. Production availability specification 263 12.4. Reliability and production availability program 264 12.4.1. Contents. 264 12.4.2. Reliability program 266 12.4.3. Production availability program 267 12.5. Validation of system reliability 267 12.5.1. Reliability data collection 267 12.5.2. Random failures 268 12.5.3. Common cause failures 268 12.6. Validation of production availability 268 12.6.1. Useful life 268 12.6.2. Reliability data 269 12.6.3. Production data 269 12.6.4. Use of production availability model 269 Appendices 271 Appendix 1. Notations and Abbreviations 273 Appendix 2. Markov Chain 283 Appendix 3. Comparison of Modeling Methods 293 Appendix 4. Solutions of Exercises. 301 Bibliography 315 Index 323

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Book SynopsisThe use of discrete-event simulation in various fields, such as in industry, logistics and public health, has really taken off over the last few decades. The implementation of discrete-event simulation does however require an understanding, and perhaps even a mastery, of precise theoretical and methodological principles. Discrete-Event Simulation presents the key concepts involved in any discrete-event simulation project, covering the most frequently used techniques for analysing data and results, the methodological and practical aspects of implementing discrete-event simulation, along with an introduction to the use of the Arena discrete-event simulation tool. This book combines the elements presented with applied examples, as well as numerous examples of simulation projects in various fields.

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Book SynopsisThis book aims to provide a synthesis of work and ideas done by our team over the last fifteen years in the field of information processing for expression of industrial performance. The statement of objectives on the one hand and the calculation of the other performances are discussed, with the search for the explanation of the link between these two basic steps of an industrial improvement. Beyond the synthetic and typological character of this study, the originality of this work lies in the consideration of the temporal dimension of the objectives, and spread on performance expressions. A fuzzy processing and multi-criteria aggregations time information that can be quantitative, qualitative or symbolic are proposed, in line with industrial practice and literature in the field of performance management.Table of ContentsForeword ix Chapter 1. The Industrial System 1 1.1. Introduction 1 1.2. The RB company’s “Hydraulic Cylinder Production” line 2 1.2.1. The Overall Equipment Effectiveness – OEE 4 1.2.2. The Non-compliance rate 5 1.2.3. The Throughput time 5 1.3. Characterization of the industrial system 6 1.3.1. General comments about systems theory 7 1.3.2. The role of the observer 12 1.3.3. Abstraction levels 13 1.3.4. Structure of the industrial system 14 1.3.5. Behavior of the industrial system 17 1.3.6. To summarize these system characteristics 23 1.4. A few words about information handling for the “Hydraulic Cylinder Production” line of the RB company 24 1.5. Objectives and systems theory 26 1.6. Summary 29 Chapter 2. Industrial Objectives: The Variable 31 2.1. Introduction 31 2.2. The objective and the variable: re-reading the tale of the chicken and the egg 34 2.3. Definition of the notion of a variable 37 2.4. When a variable becomes a criterion 42 2.5. Industrial typology 47 2.5.1. Key success factors and key performance factors 49 2.5.2. Strategic, tactical and operational variables 50 2.5.3. Action variables and state variables 51 2.5.4. Customer satisfaction, productivity and context 53 2.6. Relationships between variables: industrial practice 54 2.6.1. Hierarchical approaches 54 2.6.2. Cognitive approaches 60 2.7. Semantic and choice of a variable: the power of an intention 62 2.8. Summary 68 Chapter 3. Industrial Objectives: The Value 71 3.1. Introduction 71 3.2. A value to define the objective 73 3.3. The value and the intention 78 3.3.1. The desire-objective 78 3.3.2. The requirement-objective 80 3.3.3. Inadequacy, improvement and desire 84 3.3.4. The value, the desire-objectives and the requirement-objectives 87 3.4. The value and the time 89 3.4.1. Achieving the objective, a question of time 89 3.4.2. Some characteristics of the temporal horizon 91 3.4.3. Summary 94 3.5. The observer’s intention and the temporal horizon: converging perspectives 95 3.6. What is said about objectives 97 3.7. Summary 105 Chapter 4. Industrial Objectives: A Fuzzy Formalization to Move from Natural Language to Numbers 107 4.1. Introduction 107 4.2. The interest of using the theory of fuzzy subsets 109 4.3. When Mr. C.C. expresses himself about the Throughput time of the “Hydraulic Cylinder Production” line 113 4.4. Numbers and words 114 4.5. Graduality and fuzzy subsets 121 4.5.1. Membership function 121 4.5.2. Fuzzy meaning and description 124 4.6. Operations between fuzzy subsets 126 4.6.1. Fuzzy union, intersection and complement 126 4.6.2. Example of use of the operator of fuzzy union. 127 4.6.3. Example of use of the fuzzy intersection operator 129 4.6.4. Triangular norms 132 4.6.5. Triangular conorms 133 4.7. Imprecision of measurements and theory of possibilities 134 4.7.1. Generalities about measurement uncertainties 136 4.7.2. Confidence intervals and possibility distribution 138 4.7.3. Fuzzy descriptions of an imprecise measurement 141 4.8. Summary 144 Chapter 5. Industrial Objectives: Outlining Performance Expression 147 5.1. Introduction 147 5.2. The notion of performance 148 5.2.1. General comments 148 5.2.2. Industrial performance 151 5.3. From performance to performance expression 155 5.3.1. General comments 155 5.3.2. Semantics of performance expression 157 5.4. The process of precisiation of the finality into objectives: model and notations 159 5.4.1. Principle 160 5.4.2. From the finality to the goal variables 162 5.4.3. From goal variables to objective variables 163 5.4.4. The process of precisiation 163 5.4.5. Objective attributes 163 5.5. Computation of performance expression: our assumptions 169 5.6. Summary 171 Chapter 6. Industrial Objectives: Computation of Performance Expression of the Desire-Objective 173 6.1. Introduction 173 6.2. Returning to the notion of the desire-objective 174 6.3. “Computation” of the performance expression of a desire-objective 176 6.4. The observer expresses their “feeling” directly 178 6.5. The observer has a measurement value associated with the considered variable 179 6.6. The observer has a set of measurement values or of information associated with the considered variable 182 6.7. Looking back over computation 187 6.8. Summary 189 Chapter 7. Industrial Objectives: Computation of the Performance Expression of the Requirement-Objective 191 7.1. Introduction 191 7.2. Returning to the notion of a requirement-objective 192 7.3. A few points about the notion of scale 194 7.4. Computation of the performance expression for the improvement-objective 196 7.4.1. The observer computes a numerical performance expression 197 7.4.2. The observer computes a linguistic performance expression 204 7.4.3. Looking back over the computation 212 7.5. Computation of the performance expression of the inadequacy-objective 214 7.5.1. The observer computes a performance expression 215 7.5.2. The observer computes a performance expression and represents it visually 220 7.5.3. Looking over the computation 227 7.6. Summary 227 Conclusion 229 Bibliography 233 Index 249

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