Multivariable predictive control : applications in industry /: applications in industry. (2017)
- Record Type:
- Book
- Title:
- Multivariable predictive control : applications in industry /: applications in industry. (2017)
- Main Title:
- Multivariable predictive control : applications in industry
- Further Information:
- Note: Sandip K. Lahiri.
- Authors:
- Lahiri, Sandip Kumar, 1970-
- Contents:
- Figure List xvii Table List xix Preface xxi 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 1.7 Advantage of Implementing MPC 10 1.8 How Does MPC Extract Benefit? 13 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 2.3 Features of MPC 26 2.4 Brief Introduction to Model Predictive Control Techniques 27 3 Historical Development of Different MPC Technology 43 3.1 History of MPC Technology 43 3.2 Points to Consider While Selecting an MPC 52 4 MPC Implementation Steps 55 4.1 Implementing a MPC Controller 55 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 5.4 Case Studies 73 6 Assessment of Regulatory Base Control Layer in Plants 77 6.1 Failure Mode of Control Loops and Their Remedies 77 6.2 Control ValveFigure List xvii Table List xix Preface xxi 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 1.7 Advantage of Implementing MPC 10 1.8 How Does MPC Extract Benefit? 13 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 2.3 Features of MPC 26 2.4 Brief Introduction to Model Predictive Control Techniques 27 3 Historical Development of Different MPC Technology 43 3.1 History of MPC Technology 43 3.2 Points to Consider While Selecting an MPC 52 4 MPC Implementation Steps 55 4.1 Implementing a MPC Controller 55 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 5.4 Case Studies 73 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 6.3 Sensor Problems 82 6.4 Controller Problems 83 6.5 Process?]Related Problems 84 6.6 Human Factor 85 6.7 Control Performance Assessment/Monitoring 86 6.8 Commonly Used Control System Performance KPIs 87 6.9 Tuning for PID Controllers 92 7 Functional Design of MPC Controllers 101 7.1 What Is Functional Design? 101 7.2 Steps in Functional Design 102 8 Preliminary Process Test and Step Test 113 8.1 Pre?]Stepping, or Preliminary Process Test 113 8.2 Step Testing 115 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 9.3 The Basic Steps of System Identification 127 9.4 Model Structures 137 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 10.3 Types of Soft Sensors 147 10.4 Soft Sensors Development Methodology 149 10.5 Data?]Driven Methods for Soft Sensing 156 10.6 Open Issues and Future Steps of Soft Sensor Development 162 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 11.5 Different Steps to Build and Activate Simulator in an Offline PC 176 11.6 Example of Tests Carried out in Simulator 177 11.7 Guidelines for Choosing Tuning Parameters 181 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 13 Online Controller Tuning 193 13.1 What Is Online MPC Controller Tuning? 193 13.2 Basics of Online Tuning 193 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 14.3 Contributing Failure Factors of Postimplementation MPC Application 205 14.4 Strategies to Avoid MPC Failures 210 15 MPC Performance Monitoring 221 15.1 Why Performance Assessment of MPC Application Is Necessary 221 15.2 Types of Performance Assessment 222 15.3 Benefit Measurement after MPC Implementation 222 15.4 Parameters to Be Monitored for MPC Performance Evaluation 223 15.5 KPIs to Troubleshoot Poor Performance of Multivariable Controls 228 15.6 Exploitation of Constraints Handling and Maximization of MPC 16 Commercial MPC Vendors and Applications 235 16.1 Basic Modules and Components of Commercial MPC Software 235 16.2 Major Commercial MPC Software 243 16.3 AspenTech and DMCplus 244 16.4 RMPCT by Honeywell 251 16.5 SMOC–Shell Global Solution 253 16.6 Conclusion 261 Index … (more)
- Edition:
- 1st
- Publisher Details:
- Hoboken, New Jersey : John Wiley & Sons, Inc
- Publication Date:
- 2017
- Extent:
- 1 online resource
- Subjects:
- 629.8
Predictive control
Multivariate analysis - Languages:
- English
- ISBNs:
- 9781119243595
9781119243519 - Related ISBNs:
- 9781119243601
- Notes:
- Note: Description based on CIP data; resource not viewed.
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- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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- Physical Locations:
- British Library HMNTS - ELD.DS.176252
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