Description
Preface xiii Acknowledgments xvii Part 1 Challenges and Opportunities For Digitalization 1 1 Challenges for Operation Excellence 3 1.1 Introduction 3 1.2 Operation Activities in a Process Plant 4 1.3 The Major Challenges Facing the Industries 5 1.4 The Methodology of Connected Plant 11 1.5 Digitalization Enabling Connected Plant 12 1.6 What is the Digitalization Journey? 18 1.7 Overview of the Book Structure 19 References 21 2 Mission of Connected Plant 23 2.1 What is Connected Plant? 23 2.2 Major Functions of Connected Plant 24 2.3 Digital Twins: The Core of Connected Plant 27 2.4 Conclusions 32 References 33 3 Data Analytics for Operation Excellence 35 3.1 Introduction 35 3.2 Process Data Overview: Characteristics and Attributes 37 3.3 Unique Attributes of Process Data Analytics 39 3.4 Model Types and Characteristics 40 3.5 First Principle Modeling and its Characteristics 42 3.6 Statistic Modeling and its Characteristics 45 3.7 Optimization Models 47 3.8 Artificial Intelligence (AI) and Machine Learning (ML) Models 50 3.9 Put All Together: Digital Twin as a Data Science Platform 55 References 59 Part 2 Model Thinking For Smart Operations 63 4 Statistics Basics 65 4.1 Introduction 65 4.2 Normal Distribution 65 4.3 Conditional Probability 72 4.4 Bayes’ Probability 73 4.5 Statistic Tests 75 References 84 5 Advanced Statistic Modeling 85 5.1 Introduction 85 5.2 Distribution Models 85 5.3 Correlation Models 94 5.4 Advanced Modeling Techniques 101 5.5 Data Mining 106 5.6 Summary 107 References 107 6 Rigorous Process Modeling 109 6.1 Introduction 109 6.2 Reaction Kinetic Modeling 110 6.3 Reactor Types and Modeling 126 6.4 Integrated Kinetics and Reactor Modeling 131 6.5 Catalyst Deactivation Root Causes and Modeling 135 6.6 Distillation Modeling 136 6.7 Process System Modeling and Simulation 138 6.8 Separation Technology Overview 142 References 144 7 Linear Optimization Modeling 147 7.1 Introduction 147 7.2 Linear Optimization for Planning 148 7.3 How to Deal with Nonlinear Terms? 151 7.4 Delta Vector as Linear Approximation of Nonlinear Yield Models 154 7.5 Successive Linear Programing (SLP) Approach 159 References 160 8 Nonlinear Optimization Modeling 161 8.1 Introduction 161 8.2 Successive Quadratic Programming (SQP) Approach 162 8.3 Local Versus Global Optimum 162 8.4 Optimality Conditions 166 8.5 Nonlinear Process Optimization Model 167 8.6 Stochastic Programming 171 8.7 Simulation-Based Optimization 178 8.8 A Case Study for Process Optimization 180 8.9 Concluding Remarks 188 References 190 9 Process Control and APC Modeling 193 9.1 Introduction 193 9.2 Process Modeling in Control 194 9.3 Regulatory Control: Managing Individual Variables 207 9.4 PID Controller Modeling 211 9.5 Advanced Process Control (APC) 221 References 233 10 AI and Machine Learning Modeling 235 Amit Gupta and Frank (Xin X.) Zhu 10.1 Introduction 235 10.2 Artificial Neural Networks 235 10.3 Key Concept in ML: Perceptron 238 10.4 Machine Learning 242 10.5 Ml Applications in the Process Industry 246 References 248 Part 3 Connected Plant For Smart Operations 251 11 Connected Metering and Measurements 253 Martin Bragg 11.1 Introduction 253 11.2 Review of Metering Devices 254 11.3 Connected Metering 258 11.4 Positive-Unexpected Consequences of the Digital Economy 267 11.5 The Outlook for Connected Metering 269 11.6 Conclusions 273 References 274 12 Connected Asset and Safety Management 275 Frank (Xin X.) Zhu and Tony Downes 12.1 Introduction 275 12.2 Review of Different Maintenance Strategies 276 12.3 The Concept of Operating Windows 280 12.4 The Major Gaps in Current Asset Management 283 12.5 Digitalized Asset Management 284 12.6 Process Safety Management 290 12.7 Case Study: APM Drives Capacity Improvement 299 Reference 301 13 Integrated Production Planning and Process Control 303 13.1 Introduction 303 13.2 Current Practice in Site-Wide Optimization and Control 304 13.3 Simultaneous Approach for Site-Wide Optimization and Control 304 13.4 General Decomposition Strategy 309 13.5 MPC-Based Integration Approach 314 13.6 Rigorous Model-Based Integration Approach 322 13.7 Comparison Between the MPC and Rigorous Model-Based Approaches 324 References 325 14 Digitalizing the Energy Management 327 14.1 Introduction 327 14.2 The Concept of Energy Intensity 328 14.3 Energy Benchmarking for Processes 337 14.4 The Concept of Key Indicators 340 14.5 Set Up Targets for Key Indicators 346 14.6 Economic Evaluation for Key Indicators 350 14.7 Site-Wide Energy Management Strategy 354 14.8 Digital Twin for Energy Management 360 14.9 Establishing Energy Management System 361 References 365 15 Integrating the Workflows 367 Frank (Xin X.) Zhu and Joe Ritchie 15.1 Introduction 367 15.2 Key Elements of Industrial Supply Chain 368 15.3 Little Integration of Supply Chain Work Processes 381 15.4 Gaps Existing in Current Supply Chain Management 382 15.5 Integrated Work Process for Supply Chain Management 383 15.6 Supply Chain Digital Twin: One Platform for Workflow Integration and Automation 385 15.7 Integration of Engineering Models with Supply Chain Digital Twin 387 References 388 16 Digitalizing the Workforce 389 Rohan McAdam 16.1 Introduction 389 16.2 Enabling the Workforce 390 16.3 Empowering the Workforce 398 16.4 Digitalization Challenges 410 16.5 Summary 416 References 416 Part 4 Digital Solutions For Smart Operations 419 17 Honeywell Forge: The Platform for Connected Plant 421 Matt Burd and Frank (Xin X.) Zhu 17.1 Honeywell Forge: A Digital Platform for Connected Plant 421 17.2 IIoT for Data Infrastructure 421 17.3 How It Works? 423 17.4 Intelligent Models Behind Digital Twins in Honeywell Forge 429 17.5 Cybersecurity 434 Reference 436 18 Digital Reediness Assessment and Six-Step Digitalization Journey 437 18.1 Introduction 437 18.2 Digital Readiness Assessment 438 18.3 The Six-Step Digitalization Journey 449 18.4 Recommendations: A Digital Transformation Management System 454 18.5 Establishing a Digital Transformation Management System 455 References 457 19 Digital Project Evaluation and Development 459 19.1 Introduction 459 19.2 Business Case Evaluation 459 19.3 Digital Project Development Steps 461 19.4 Remarks on Digital Project Development 465 19.5 S-Curve for Project Review and Management 469 19.6 Basics of Economic Analysis 472 Reference 475 20 Application Case Studies 477 20.1 Introduction 477 20.2 Application Cases from Digital Twins 477 20.3 Applications from Other Digital Projects 481 References 506 Index 507
