Multivariable predictive control : applications in industry / Sandip Kumar Lahiri.

Format:

Book

Author/Creator:

Lahiri, Sandip Kumar, 1970- author.

Language:

English

Subjects (All):

Predictive control.

Multivariate analysis.

Physical Description:

1 online resource (304 pages) : illustrations (some color), tables

Edition:

1st ed.

Place of Publication:

Hoboken, New Jersey ; Chichester, West Sussex, England : Wiley, 2017.

Summary:

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.

Contents:

Intro

Title Page

Copyright Page

Contents

Figure List

Table List

Preface

Chapter 1 Introduction of Model Predictive Control

1.1 Purpose of Process Control in Chemical Process Industries (CPI)

1.2 Shortcomings of Simple Regulatory PID Control

1.3 What Is Multivariable Model Predictive Control?

1.4 Why Is a Multivariable Model Predictive Optimizing Controller Necessary?

1.5 Relevance of Multivariable Predictive Control (MPC) in Chemical Process Industry in Today's Business Environment

1.6 Position of MPC in Control Hierarchy

1.6.1 Regulatory PID Control Layer

1.6.2 Advance Regulatory Control (ARC) Layer

1.6.3 Multivariable Model-Based Control

1.6.4 Economic Optimization Layer

1.6.4.1 First Layer of Optimization

1.6.4.2 Second Layer of Optimization

1.6.4.3 Third Layer of Optimization

1.7 Advantage of Implementing MPC

1.8 How Does MPC Extract Benefit?

1.8.1 MPC Inherent Stabilization Effect

1.8.2 Process Interactions

1.8.3 Multiple Constraints

1.8.4 Intangible Benefits of MPC

1.9 Application of MPC in Oil Refinery, Petrochemical, Fertilizer, and Chemical Plants, and Related Benefits

References

Chapter 2 Theoretical Base of MPC

2.1 Why MPC?

2.2 Variables Used in MPC

2.2.1 Manipulated Variables (MVs)

2.2.2 Controlled Variables (CVs)

2.2.3 Disturbance Variables (DVs)

2.3 Features of MPC

2.3.1 MPC Is a Multivariable Controller

2.3.2 MPC Is a Model Predictive Controller

2.3.3 MPC Is a Constrained Controller

2.3.4 MPC Is an Optimizing Controller

2.3.5 MPC Is a Rigorous Controller

2.4 Brief Introduction to Model Predictive Control Techniques

2.4.1 Simplified Dynamic Control Strategy of MPC

2.4.2 Step 1: Read Process Input and Output

2.4.3 Step 2: Prediction of CVs

2.4.3.1 Building Dynamic Process Model.

2.4.3.2 How MPC Predicts the Future

2.4.4 Step 3: Model Reconciliation

2.4.5 Step 4: Determine the Size of the Control Process

2.4.6 Step 5: Removal of Ill-Conditioned Problems

2.4.7 Step 6: Optimum Steady-State Targets

2.4.8 Step 7: Develop Detailed Plan of MV Movement

Chapter 3 Historical Development of Different MPC Technology

3.1 History of MPC Technology

3.1.1 Pre-Era

3.1.1.1 Developer

3.1.1.2 Motivation

3.1.1.3 Limitations

3.1.2 First Generation of MPC (1970-1980)

3.1.2.1 Characteristics of First-Generation MPC Technology

3.1.2.2 IDCOM Algorithm and Its Features

3.1.2.3 DMC Algorithm and Its Features

3.1.3 Second-Generation MPC (1980-1985)

3.1.4 Third-Generation MPC (1985-1990)

3.1.4.1 Distinguishing Features of Third-Generation MPC Algorithm

3.1.4.2 Distinguishing Features of the IDCOM-M Algorithm

3.1.4.3 Evolution of SMOC

3.1.4.4 Distinctive Features of SMOC

3.1.5 Fourth-Generation MPC (1990-2000)

3.1.5.1 Distinctive Features of Fourth-Generation MPC

3.1.6 Fifth-Generation MPC (2000-2015)

3.2 Points to Consider While Selecting an MPC

Chapter 4 MPC Implementation Steps

4.1 Implementing a MPC Controller

4.1.1 Step 1: Preliminary Cost-Benefit Analysis

4.1.2 Step 2: Assessment of Base Control Loops

4.1.3 Step 3: Functional Design of Controller

4.1.4 Step 4: Conduct the Preliminary Plant Test (Pre-Stepping)

4.1.5 Step 5: Conduct the Plant Step Test

4.1.6 Step 6: Identify a Process Model

4.1.7 Step 7: Generate Online Soft Sensors or Virtual Sensors

4.1.8 Step 8: Perform Offline Controller Simulation/Tuning

4.1.9 Step 9: Commission the Online Controller

4.1.10 Step 10: Online MPC Controller Tuning

4.1.11 Step 11: Hold Formal Operator Training

4.1.12 Step 12: Performance Monitoring of MPC Controller.

4.1.13 Step 13: Maintain the MPC Controller

4.2 Summary of Steps Involved in MPC Projects with Vendor

Chapter 5 Cost-Benefit Analysis of MPC before Implementation

5.1 Purpose of Cost-Benefit Analysis of MPC before Implementation

5.2 Overview of Cost-Benefit Analysis Procedure

5.3 Detailed Benefit Estimation Procedures

5.3.1 Initial Screening for Suitability of Process to Implement MPC

5.3.2 Process Analysis and Economics Analysis

5.3.3 Understand the Constraints

5.3.4 Identify Qualitatively Potential Area of Opportunities

5.3.4.1 Example 1: Air Separation Plant

5.3.4.2 Example 2: Distillation Columns

5.3.5 Collect All Relevant Plant and Economic Data (Trends, Records)

5.3.6 Calculate the Standard Deviation and Define the Limit

5.3.7 Estimate the Stabilizing Effect of MPC and Shift in the Average

5.3.7.1 Benefit Estimation: When the Constraint Is Known

5.3.7.2 Benefit Estimation: When the Constraint Is Not Well Known or Changing

5.3.8 Estimate Change in Key Performance Parameters Such as Yield, Throughput, and Energy Consumption

5.3.8.1 Example: Ethylene Oxide Reactor

5.3.9 Identify How This Effect Translates to Plant Profit Margin

5.3.10 Estimate the Economic Value of the Effect

5.4 Case Studies

5.4.1 Case Study 1

5.4.1.1 Benefit Estimation Procedure

5.4.2 Case Study 2

5.4.2.1 Benefit Estimation Procedure

Chapter 6 Assessment of Regulatory Base Control Layer in Plants

6.1 Failure Mode of Control Loops and Their Remedies

6.2 Control Valve Problems

6.2.1 Improper Valve Sizing

6.2.1.1 How to Detect a Particular Control Valve Sizing Problem

6.2.2 Valve Stiction

6.2.2.1 What Is Control Valve Stiction?

6.2.2.2 How to Detect Control Valve Stiction Online

6.2.2.3 Combating Stiction.

6.2.2.4 Techniques for Combating Stiction Online

6.2.3 Valve Hysteresis and Backlash

6.3 Sensor Problems

6.3.1 Noisy

6.3.2 Flatlining

6.3.3 Scale/Range

6.3.4 Calibration

6.3.5 Overfiltered

6.4 Controller Problems

6.4.1 Poor Tuning and Lack of Maintenance

6.4.2 Poor or Missing Feedforward Compensation

6.4.3 Inappropriate Control Structure

6.5 Process-Related Problems

6.5.1 Problems of Variable Gain

6.5.2 Oscillations

6.5.2.1 Variable Valve Gain

6.5.2.2 Variable Process Gain

6.6 Human Factor

6.7 Control Performance Assessment/Monitoring

6.7.1 Available Software for Control Performance Monitoring

6.7.2 Basic Assessment Procedure

6.8 Commonly Used Control System Performance KPIs

6.8.1 Traditional Indices

6.8.1.1 Peak Overshoot Ratio (POR)

6.8.1.2 Decay Rate

6.8.1.3 Peak Time and Rise Time

6.8.1.4 Settling Time

6.8.1.5 Integral of Error Indexes

6.8.2 Simple Statistical Indices

6.8.2.1 Mean of Control Error (%)

6.8.2.2 Standard Deviation of Control Error (%)

6.8.2.3 Standard Variation of Control Error (%)

6.8.2.4 Standard Deviation of Controller Output (%)

6.8.2.5 Skewness of Control Error

6.8.2.6 Kurtosis of Control Error

6.8.2.7 Ratio of Standard of Control Error and Controller Output

6.8.2.8 Maximum Bicoherence

6.8.3 Business/Operational Metrics

6.8.3.1 Loop Health

6.8.3.2 Service Factor

6.8.3.3 Key Performance Indicators

6.8.3.4 Operational Performance Efficiency Factor

6.8.3.5 Overall Loop Performance Index

6.8.3.6 Controller Output Changes in Manual

6.8.3.7 Mode Changes

6.8.3.8 Totalized Valve Reversals and Valve Travel

6.8.3.9 Process Model Parameters

6.8.4 Advanced Indices

6.8.4.1 Harris Index

6.8.4.2 Nonlinearity Index

6.8.4.3 Oscillation-Detection Indices

6.8.4.4 Disturbance Detection Indices.

6.8.4.5 Autocorrelation Indices

6.9 Tuning for PID Controllers

6.9.1 Complications with Tuning PID Controllers

6.9.2 Loop Retuning

6.9.3 Classical Controller Tuning Algorithms

6.9.3.1 Controller Tuning Methods

6.9.3.2 Ziegler-Nichols Tuning Method

6.9.3.3 Dahlin (Lambda) Tuning Method

6.9.4 Manual Controller Tuning Methods in Absence of Any Software

6.9.4.1 Pre-Tuning

6.9.4.2 Bring in Baseline Parameters

6.9.4.3 Some Like It Simple

6.9.4.4 Tuning Cascade Control

Chapter 7 Functional Design of MPC Controllers

7.1 What Is Functional Design?

7.2 Steps in Functional Design

7.2.1 Step 1: Define Process Control Objectives

7.2.1.1 Economic Objectives

7.2.1.2 Operating Objectives

7.2.1.3 Control Objectives

7.2.2 Step 2: Identify Process Constraints

7.2.2.1 Process Limitations

7.2.2.2 Safety Limitations

7.2.2.3 Process Instrument Limitations

7.2.2.4 Raw Material and Utility Supply Limitation

7.2.2.5 Product Limitations

7.2.3 Step 3: Define Controller Scope

7.2.4 Step 4: Select the Variables

7.2.4.1 Economics of the Unit

7.2.4.2 Constraints of the Unit

7.2.4.3 Control of the Unit

7.2.4.4 Manipulated Variables (MVs)

7.2.4.5 Controlled Variables (CVs)

7.2.4.6 Disturbance Variables (DVs)

7.2.4.7 Practical Guidelines for Variable Selections

7.2.5 Step 5: Rectify Regulatory Control Issues

7.2.5.1 Practical Guidelines for Changing Regulatory Controller Strategy

7.2.6 Step 6: Explore the Scope of Inclusions of Inferential Calculations

7.2.7 Step 7: Evaluate Potential Optimization Opportunity

7.2.7.1 Practical Guidelines for Finding out Optimization Opportunities

7.2.8 Step 8: Define LP or QP Objective Function

7.2.8.1 CDU Example

Chapter 8 Preliminary Process Test and Step Test.

8.1 Pre-Stepping, or Preliminary Process Test.

Notes:

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

9781119243595

1119243599

9781119243519

1119243513

9781119243434

1119243432

OCLC:

976036412