A 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 references, plant engineers are often at a loss as to how to manage and maintain MPC systems once the applications have been installed and the consultants and vendors' reps have left the plant. Written by a chemical engineer with two decades of experience in operations and technical services at petrochemical companies, this book fills that regrettable gap in the professional literature.

• Provides a cost-benefit analysis of typical MPC projects and reviews commercially available MPC software packages
• Details software implementation steps, as well as techniques for successfully evaluating and monitoring software performance once it has been installed
• Features case studies and real-world examples from industries, worldwide, illustrating the advantages and common pitfalls of MPC systems
• Describes MPC application failures in an array of companies, exposes the root causes of those failures, and offers proven safeguards and corrective measures for avoiding similar failures

Multivariable Predictive Control: Applications in Industry is an indispensable resource for plant process engineers and control engineers working in chemical plants, petrochemical companies, and oil refineries in which MPC systems already are operational, or where MPC implementations are being considering.

Sandip Kumar Lahiri, PhD, is a chemical engineer with more than twenty one years of experience in operations and technical services at leading petrochemical industries around the globe. His areas of expertise include simulation, process modelling, artificial intelligence and neural networks in process industry, APC, soft sensor, and slurry flow modelling.

Figure 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 Constrai…