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E-grāmata: Multivariable Predictive Control: Applications in Industry

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  • Izdošanas datums: 10-Aug-2017
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119243519
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Multivariable Predictive Control: Applications in IndustryPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 10-Aug-2017
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781119243519
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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.

Figure List xix
Table List xxi
Preface xxiii
1 Introduction of Model Predictive Control 1(22)
1.1 Purpose of Process Control in Chemical Process Industries (CPI)
1(1)
1.2 Shortcomings of Simple Regulatory PID Control
2(1)
1.3 What Is Multivariable Model Predictive Control?
3(1)
1.4 Why Is a Multivariable Model Predictive Optimizing Controller Necessary?
4(2)
1.5 Relevance of Multivariable Predictive Control (MPC) in Chemical Process Industry in Today's Business Environment
6(1)
1.6 Position of MPC in Control Hierarchy
6(4)
1.6.1 Regulatory PID Control Layer
6(2)
1.6.2 Advance Regulatory Control (ARC) Layer
8(1)
1.6.3 Multivariable Model-Based Control
8(1)
1.6.4 Economic Optimization Layer
8(5)
1.6.4.1 First Layer of Optimization
8(1)
1.6.4.2 Second Layer of Optimization
9(1)
1.6.4.3 Third Layer of Optimization
9(1)
1.7 Advantage of Implementing MPC
10(3)
1.8 How Does MPC Extract Benefit?
13(4)
1.8.1 MPC Inherent Stabilization Effect
13(1)
1.8.2 Process Interactions
14(1)
1.8.3 Multiple Constraints
15(2)
1.8.4 Intangible Benefits of MPC
17(1)
1.9 Application of MPC in Oil Refinery, Petrochemical, Fertilizer, and Chemical Plants, and Related Benefits
17(6)
2 Theoretical Base of MPC 23(20)
2.1 Why MPC?
23(2)
2.2 Variables Used in MPC
25(1)
2.2.1 Manipulated Variables (MVs)
25(1)
2.2.2 Controlled Variables (CVs)
25(1)
2.2.3 Disturbance Variables (DVs)
25(1)
2.3 Features of MPC
26(1)
2.3.1 MPC Is a Multivariable Controller
26(1)
2.3.2 MPC Is a Model Predictive Controller
26(1)
2.3.3 MPC Is a Constrained Controller
26(1)
2.3.4 MPC Is an Optimizing Controller
27(1)
2.3.5 MPC Is a Rigorous Controller
27(1)
2.4 Brief Introduction to Model Predictive Control Techniques
27(16)
2.4.1 Simplified Dynamic Control Strategy of MPC
28(1)
2.4.2 Step 1: Read Process Input and Output
29(1)
2.4.3 Step 2: Prediction of CVs
30(3)
2.4.3.1 Building Dynamic Process Model
30(2)
2.4.3.2 How MPC Predicts the Future
32(1)
2.4.4 Step 3: Model Reconciliation
33(1)
2.4.5 Step 4: Determine the Size of the Control Process
34(1)
2.4.6 Step 5: Removal of Ill-Conditioned Problems
34(1)
2.4.7 Step 6: Optimum Steady-State Targets
35(1)
2.4.8 Step 7: Develop Detailed Plan of MV Movement
36(7)
3 Historical Development of Different MPC Technology 43(12)
3.1 History of MPC Technology
43(9)
3.1.1 Pre-Era
43(1)
3.1.1.1 Developer
43(1)
3.1.1.2 Motivation
44(1)
3.1.1.3 Limitations
44(1)
3.1.2 First Generation of MPC (1970-1980)
44(2)
3.1.2.1 Characteristics of First-Generation MPC Technology
44(1)
3.1.2.2 IDCOM Algorithm and Its Features
45(1)
3.1.2.3 DMC Algorithm and Its Features
46(1)
3.1.3 Second-Generation MPC (1980-1985)
46(1)
3.1.4 Third-Generation MPC (1985-1990)
47(3)
3.1.4.1 Distinguishing Features of Third-Generation MPC Algorithm
48(1)
3.1.4.2 Distinguishing Features of the IDCOM-M Algorithm
49(1)
3.1.4.3 Evolution of SMOC
50(1)
3.1.4.4 Distinctive Features of SMOC
50(1)
3.1.5 Fourth-Generation MPC (1990-2000)
50(1)
3.1.5.1 Distinctive Features of Fourth-Generation MPC
51(1)
3.1.6 Fifth-Generation MPC (2000-2015)
51(1)
3.2 Points to Consider While Selecting an MPC
52(3)
4 MPC Implementation Steps 55(8)
4.1 Implementing a MPC Controller
55(5)
4.1.1 Step 1: Preliminary Cost-Benefit Analysis
55(1)
4.1.2 Step 2: Assessment of Base Control Loops
55(1)
4.1.3 Step 3: Functional Design of Controller
56(1)
4.1.4 Step 4: Conduct the Preliminary Plant Test (Pre-Stepping)
57(1)
4.1.5 Step 5: Conduct the Plant Step Test
57(1)
4.1.6 Step 6: Identify a Process Model
57(1)
4.1.7 Step 7: Generate Online Soft Sensors or Virtual Sensors
58(1)
4.1.8 Step 8: Perform Offline Controller Simulation/Tuning
58(1)
4.1.9 Step 9: Commission the Online Controller
58(1)
4.1.10 Step 10: Online MPC Controller Tuning
59(1)
4.1.11 Step 11: Hold Formal Operator Training
59(1)
4.1.12 Step 12: Performance Monitoring of MPC Controller
59(1)
4.1.13 Step 13: Maintain the MPC Controller
60(1)
4.2 Summary of Steps Involved in MPC Projects with Vendor
60(3)
5 Cost-Benefit Analysis of MPC before Implementation 63(14)
5.1 Purpose of Cost-Benefit Analysis of MPC before Implementation
63(1)
5.2 Overview of Cost-Benefit Analysis Procedure
64(1)
5.3 Detailed Benefit Estimation Procedures
65(8)
5.3.1 Initial Screening for Suitability of Process to Implement MPC
65(1)
5.3.2 Process Analysis and Economics Analysis
66(1)
5.3.3 Understand the Constraints
67(1)
5.3.4 Identify Qualitatively Potential Area of Opportunities
67(2)
5.3.4.1 Example 1: Air Separation Plant
68(1)
5.3.4.2 Example 2: Distillation Columns
69(1)
5.3.5 Collect All Relevant Plant and Economic Data (Trends, Records)
69(1)
5.3.6 Calculate the Standard Deviation and Define the Limit
69(1)
5.3.7 Estimate the Stabilizing Effect of MPC and Shift in the Average
70(2)
5.3.7.1 Benefit Estimation: When the Constraint Is Known
71(1)
5.3.7.2 Benefit Estimation: When the Constraint Is Not Well Known or Changing
72(1)
5.3.8 Estimate Change in Key Performance Parameters Such as Yield, Throughput, and Energy Consumption
72(1)
5.3.8.1 Example: Ethylene Oxide Reactor
72(1)
5.3.9 Identify How This Effect Translates to Plant Profit Margin
73(1)
5.3.10 Estimate the Economic Value of the Effect
73(1)
5.4 Case Studies
73(4)
5.4.1 Case Study 1
73(1)
5.4.1.1 Benefit Estimation Procedure
73(1)
5.4.2 Case Study 2
74(3)
5.4.2.1 Benefit Estimation Procedure
74(3)
6 Assessment of Regulatory Base Control Layer in Plants 77(24)
6.1 Failure Mode of Control Loops and Their Remedies
77(1)
6.2 Control Valve Problems
77(5)
6.2.1 Improper Valve Sizing
78(1)
6.2.1.1 How to Detect a Particular Control Valve Sizing Problem
78(1)
6.2.2 Valve Stiction
79(2)
6.2.2.1 What Is Control Valve Stiction?
79(1)
6.2.2.2 How to Detect Control Valve Stiction Online
80(1)
6.2.2.3 Combating Stiction
80(1)
6.2.2.4 Techniques for Combating Stiction Online
80(1)
6.2.3 Valve Hysteresis and Backlash
81(1)
6.3 Sensor Problems
82(1)
6.3.1 Noisy
82(1)
6.3.2 Flatlining
82(1)
6.3.3 Scale/Range
82(1)
6.3.4 Calibration
82(1)
6.3.5 Overfiltered
83(1)
6.4 Controller Problems
83(1)
6.4.1 Poor Tuning and Lack of Maintenance
83(1)
6.4.2 Poor or Missing Feedforward Compensation
83(1)
6.4.3 Inappropriate Control Structure
84(1)
6.5 Process-Related Problems
84(1)
6.5.1 Problems of Variable Gain
84(1)
6.5.2 Oscillations
84(2)
6.5.2.1 Variable Valve Gain
85(1)
6.5.2.2 Variable Process Gain
85(1)
6.6 Human Factor
85(1)
6.7 Control Performance Assessment/Monitoring
86(1)
6.7.1 Available Software for Control Performance Monitoring
86(1)
6.7.2 Basic Assessment Procedure
87(1)
6.8 Commonly Used Control System Performance KPIs
87(5)
6.8.1 Traditional Indices
88(1)
6.8.1.1 Peak Overshoot Ratio (POR)
88(1)
6.8.1.2 Decay Rate
88(1)
6.8.1.3 Peak Time and Rise Time
88(1)
6.8.1.4 Settling Time
88(1)
6.8.1.5 Integral of Error Indexes
88(1)
6.8.2 Simple Statistical Indices
88(2)
6.8.2.1 Mean of Control Error (%)
89(1)
6.8.2.2 Standard Deviation of Control Error (%)
89(1)
6.8.2.3 Standard Variation of Control Error (%)
89(1)
6.8.2.4 Standard Deviation of Controller Output (%)
89(1)
6.8.2.5 Skewness of Control Error
89(1)
6.8.2.6 Kurtosis of Control Error
89(1)
6.8.2.7 Ratio of Standard of Control Error and Controller Output
89(1)
6.8.2.8 Maximum Bicoherence
90(1)
6.8.3 Business/Operational Metrics
90(1)
6.8.3.1 Loop Health
90(1)
6.8.3.2 Service Factor
90(1)
6.8.3.3 Key Performance Indicators
90(1)
6.8.3.4 Operational Performance Efficiency Factor
90(1)
6.8.3.5 Overall Loop Performance Index
90(1)
6.8.3.6 Controller Output Changes in Manual
90(1)
6.8.3.7 Mode Changes
90(1)
6.8.3.8 Totalized Valve Reversals and Valve Travel
90(1)
6.8.3.9 Process Model Parameters
90(1)
6.8.4 Advanced Indices
90(2)
6.8.4.1 Harris Index
91(1)
6.8.4.2 Nonlinearity Index
91(1)
6.8.4.3 Oscillation-Detection Indices
91(1)
6.8.4.4 Disturbance Detection Indices
92(1)
6.8.4.5 Autocorrelation Indices
92(1)
6.9 Tuning for PID Controllers
92(9)
6.9.1 Complications with Tuning PID Controllers
93(1)
6.9.2 Loop Retuning
93(1)
6.9.3 Classical Controller Tuning Algorithms
94(1)
6.9.3.1 Controller Tuning Methods
94(1)
6.9.3.2 Ziegler-Nichols Tuning Method
94(1)
6.9.3.3 Dahlin (Lambda) Tuning Method
94(1)
6.9.4 Manual Controller Tuning Methods in Absence of Any Software
95(7)
6.9.4.1 Pre-Tuning
95(2)
6.9.4.2 Bring in Baseline Parameters
97(1)
6.9.4.3 Some Like It Simple
97(1)
6.9.4.4 Tuning Cascade Control
98(3)
7 Functional Design of MPC Controllers 101(12)
7.1 What Is Functional Design?
101(1)
7.2 Steps in Functional Design
102(11)
7.2.1 Step 1: Define Process Control Objectives
102(2)
7.2.1.1 Economic Objectives
102(1)
7.2.1.2 Operating Objectives
103(1)
7.2.1.3 Control Objectives
104(1)
7.2.2 Step 2: Identify Process Constraints
104(1)
7.2.2.1 Process Limitations
104(1)
7.2.2.2 Safety Limitations
104(1)
7.2.2.3 Process Instrument Limitations
105(1)
7.2.2.4 Raw Material and Utility Supply Limitation
105(1)
7.2.2.5 Product Limitations
105(1)
7.2.3 Step 3: Define Controller Scope
105(1)
7.2.4 Step 4: Select the Variables
106(3)
7.2.4.1 Economics of the Unit
106(1)
7.2.4.2 Constraints of the Unit
107(1)
7.2.4.3 Control of the Unit
107(1)
7.2.4.4 Manipulated Variables (MVs)
107(1)
7.2.4.5 Controlled Variables (CVs)
107(1)
7.2.4.6 Disturbance Variables (DVs)
108(1)
7.2.4.7 Practical Guidelines for Variable Selections
108(1)
7.2.5 Step 5: Rectify Regulatory Control Issues
109(1)
7.2.5.1 Practical Guidelines for Changing Regulatory Controller Strategy
109(1)
7.2.6 Step 6: Explore the Scope of Inclusions of Inferential Calculations
110(1)
7.2.7 Step 7: Evaluate Potential Optimization Opportunity
110(1)
7.2.7.1 Practical Guidelines for Finding out Optimization Opportunities
111(1)
7.2.8 Step 8: Define LP or QP Objective Function
111(2)
7.2.8.1 CDU Example
112(1)
8 Preliminary Process Test and Step Test 113(10)
8.1 Pre-Stepping, or Preliminary Process Test
113(2)
8.1.1 What Is Pre-Stepping?
113(1)
8.1.2 Objective of Pre-Stepping
113(1)
8.1.3 Prerequisites of Pre-Stepping
113(1)
8.1.4 Pre-Stepping
114(1)
8.2 Step Testing
115(5)
8.2.1 What Is a Step Test?
115(1)
8.2.2 What Is the Purpose of a Step Test?
115(1)
8.2.3 Details of Step Testing
116(1)
8.2.3.1 Administrative Aspects
116(1)
8.2.3.2 Technical Aspects
116(1)
8.2.4 Different Step-Testing Method
117(1)
8.2.4.1 Manual Step Testing
117(1)
8.2.4.2 PRBS (Pseudo Random Binary Sequence)
117(1)
8.2.4.3 General Guidelines of PRBS Test
117(1)
8.2.5 Difference between Normal Step Testing and PRBS Testing
118(1)
8.2.6 Which One to Choose?
118(1)
8.2.7 Dos and Don'ts of Step Testing
118(2)
8.3 Development of Step-Testing Methodology over the Years
120(3)
9 Model Building and System Identification 123(22)
9.1 Introduction to Model Building
123(1)
9.2 Key Issues in Model Identifications
124(3)
9.2.1 Identification Test
124(1)
9.2.2 Model Structure and Parameter Estimation
125(1)
9.2.3 Order Selection
126(1)
9.2.4 Model Validation
127(1)
9.3 The Basic Steps of System Identification
127(10)
9.3.1 Step 0: Experimental Design and Execution
128(2)
9.3.2 Step 1: Plan the Case that Needs to Be Modeled
130(1)
9.3.2.1 Action 1
130(1)
9.3.2.2 Action 2
130(1)
9.3.3 Step 2: Identify Good Slices of Data
130(1)
9.3.3.1 Looking at the Data
131(1)
9.3.4 Step 3: Pre-Processing of Data
131(1)
9.3.5 Step 4: Identification of Model Curve
132(4)
9.3.5.1 Hybrid Approach to System Identification
132(1)
9.3.5.2 Direct Modeling Approach of System Identification
133(1)
9.3.5.3 Subspace Identification
134(1)
9.3.5.4 Detailed Steps of Implementations
135(1)
9.3.6 Step 5: Select Final Model
136(1)
9.4 Model Structures
137(5)
9.4.1 FIR Models
138(1)
9.4.1.1 FIR Structures
138(1)
9.4.2 Prediction Error Models (PEM Models)
139(1)
9.4.2.1 PEM Structures
139(1)
9.4.3 Model for Order and Variance Reduction
140(1)
9.4.3.1 ARX Parametric Models (Discrete Time)
140(1)
9.4.3.2 Output Error Models (Discrete Time)
140(1)
9.4.3.3 Laplace Domain Parametric Models
141(1)
9.4.3.4 Final Model Form
141(1)
9.4.4 State-Space Models
141(1)
9.4.5 How to Know Which Structure and Method to Use
142(1)
9.5 Common Features of Commercial Identification Packages
142(3)
10 Soft Sensors 145(22)
10.1 What Is a Soft Sensor?
145(1)
10.2 Why Soft Sensors Are Necessary
145(2)
10.2.1 Process Monitoring and Process Fault Detection
146(1)
10.2.2 Sensor Fault Detection and Reconstruction
146(1)
10.2.3 Use of Soft Sensors in MPC Application
146(1)
10.3 Types of Soft Sensors
147(2)
10.3.1 First Principle-Based Soft Sensors
147(1)
10.3.1.1 Advantages
147(1)
10.3.1.2 Disadvantages
147(1)
10.3.2 Data-Driven Soft Sensors
148(1)
10.3.2.1 Advantages
148(1)
10.3.2.2 Disadvantages
148(1)
10.3.3 Gray Model-Based Soft Sensors
148(1)
10.3.3.1 Advantages
149(1)
10.3.4 Hybrid Model-Based Soft Sensors
149(1)
10.3.4.1 Advantages
149(1)
10.4 Soft Sensors Development Methodology
149(7)
10.4.1 Data Collection and Data Inspection
149(1)
10.4.2 Data Preprocessing and Data Conditioning
150(3)
10.4.2.1 Outlier Detection and Replacement
151(1)
10.4.2.2 Univariate Approach to Detect Outliers
151(1)
10.4.2.3 Multivariate Approach to Detect Outliers (Lin 2007)
151(1)
10.4.2.4 Handling of Missing Data
152(1)
10.4.3 Selection of Relevant Input Output Variables
153(1)
10.4.4 Data Alignment
153(1)
10.4.5 Model Selection, Training, and Validation (Kadlec 2009; Lin 2007)
153(1)
10.4.6 Analyze Process Dynamics
154(1)
10.4.7 Deployment and Maintenance
155(1)
10.5 Data-Driven Methods for Soft Sensing
156(6)
10.5.1 Principle Component Analysis
156(1)
10.5.1.1 The Basics of PCA
156(1)
10.5.1.2 Why Do We Need to Rotate the Data?
156(1)
10.5.1.3 How Do We Generate Principal Components?
156(1)
10.5.1.4 Steps to Calculating Principal Components
157(1)
10.5.2 Partial Least Squares
157(1)
10.5.3 Artificial Neural Networks
158(2)
10.5.3.1 Network Architecture
159(1)
10.5.3.2 Back Propagation Algorithm (BPA)
159(1)
10.5.4 Neuro-Fuzzy Systems
160(1)
10.5.5 Support Vector Machines
161(1)
10.5.5.1 Support Vector Regression-Based Modeling
161(1)
10.6 Open Issues and Future Steps of Soft Sensor Development
162(5)
10.6.1 Large Effort Required for Preprocessing of Industrial Data
162(1)
10.6.2 Which Modeling Method to Choose?
163(1)
10.6.3 Agreement of the Developed Model with Physics of the Process
163(1)
10.6.4 Performance Deterioration of Developed Soft Sensor Model
163(4)
11 Offline Simulation 167(16)
11.1 What Is Offline Simulation?
167(1)
11.2 Purpose of Offline Simulation
167(1)
11.3 Main Task of Offline Simulation
168(1)
11.4 Understanding Different Tuning Parameters of Offline Simulations
168(8)
11.4.1 Tuning Parameters for CVs
169(2)
11.4.1.1 Methods for Handling of Infeasibility
170(1)
11.4.1.2 Priority Ranking of CVs
170(1)
11.4.1.3 CV Give-Up
170(1)
11.4.1.4 CV Error Weight
170(1)
11.4.2 Tuning Parameters for MVs
171(1)
11.4.2.1 MV Maximum Movement Limits or Rate-of-Change Limits
171(1)
11.4.2.2 Movement Weights
171(1)
11.4.3 Tuning Parameters for Optimizer
172(3)
11.4.3.1 Economic Optimization
172(1)
11.4.3.2 General Form of Objective Function
173(1)
11.4.3.3 Weighting Coefficients
173(1)
11.4.3.4 Setting Linear Objective Coefficients
173(1)
11.4.3.5 Optimization Horizon and Optimization Speed Factor
174(1)
11.4.3.6 Optimization Speed Factor
174(1)
11.4.3.7 MV Optimization Priority
174(1)
11.4.4 Soft Limits
175(2)
11.4.4.1 How Soft Limits Work
175(1)
11.4.4.2 CV Soft Limits
175(1)
11.4.4.3 MV Soft Limits
176(1)
11.5 Different Steps to Build and Activate Simulator in an Offline PC
176(1)
11.6 Example of Tests Carried out in Simulator
177(4)
11.6.1 Control and Optimization Objectives
177(4)
11.6.1.1 Test 1
178(1)
11.6.1.2 Test 2
179(1)
11.6.1.3 Test 3
179(1)
11.6.1.4 Test 4
180(1)
11.6.1.5 Test 5
180(1)
11.6.1.6 Test 6
180(1)
11.6.1.7 Others Tests
181(1)
11.7 Guidelines for Choosing Tuning Parameters
181(2)
11.7.1 Guidelines for Choosing Initial Values
181(1)
11.7.2 How to Select Maximum Move Size and MV Movement Weights During Simulation Study
182(1)
12 Online Deployment of MPC Application in Real Plants 183(10)
12.1 What Is Online Deployment (Controller Commissioning)?
183(1)
12.2 Steps for Controller Commissioning
183(10)
12.2.1 Set up the Controller Configuration and Final Review of the Model
183(1)
12.2.2 Build the Controller
184(1)
12.2.3 Load Operator Station on PC Near the Panel Operator
184(2)
12.2.4 Take MPC Controller in Line with Prediction Mode
186(1)
12.2.5 Put the MPC Controller in Close Loop with One CV at a Time
187(1)
12.2.6 Observe MPC Controller Performance
187(2)
12.2.7 Put Optimizer in Line and Observe Optimizer Performance
189(1)
12.2.8 Evaluate Overall Controller Performance
189(1)
12.2.9 Perform Online Tuning and Troubleshooting
190(1)
12.2.10 Train Operators and Engineers on Online Platform
190(1)
12.2.11 Document MPC Features
190(1)
12.2.12 Maintain the MPC Controller
191(2)
13 Online Controller Tuning 193(6)
13.1 What Is Online MPC Controller Tuning?
193(1)
13.2 Basics of Online Tuning
193(2)
13.2.1 Key Checkout Regarding Controller Performance
193(1)
13.2.2 Steps to Troubleshoot the Problem
194(1)
13.3 Guidelines to Choose Different Tuning Parameters
195(4)
14 Why Do Some MPC Applications Fail? 199(22)
14.1 What Went Wrong?
199(2)
14.2 Failure to Build Efficient MPC Application
201(4)
14.2.1 Historical Perspective
201(1)
14.2.2 Capability of MPC Software to Capture Benefits
202(1)
14.2.3 Expertise of Implementation Team
202(2)
14.2.3.1 MPC Vendor Limitations
203(1)
14.2.3.2 Client Limitations
204(1)
14.2.4 Reliability of APC Project Methodology
204(1)
14.3 Contributing Failure Factors of Postimplementation MPC Application
205(5)
14.3.1 Technical Failure Factors
206(2)
14.3.1.1 Lack of Performance Monitoring of MPC Application
206(1)
14.3.1.2 Unresolved Basic Control Problems
206(1)
14.3.1.3 Poor Tuning and Degraded Model Quality
207(1)
14.3.1.4 Problems Related to Controller Design
207(1)
14.3.1.5 Significant Process Modifications and Enhancement
207(1)
14.3.2 Nontechnical Failure Factors
208(2)
14.3.2.1 Lack of Properly Trained Personnel
208(1)
14.3.2.2 Lack of Standards and Guidelines to MPC Support Personnel
208(1)
14.3.2.3 Lack of Organizational Collaboration and Alignment
208(1)
14.3.2.4 Poor Management of Control System
209(1)
14.4 Strategies to Avoid MPC Failures
210(11)
14.4.1 Technical Solutions
211(3)
14.4.1.1 Development of Online Performance Monitoring of APC Applications
211(1)
14.4.1.2 Improvement of Base Control Layer
212(1)
14.4.1.3 Tuning Basic Controls
212(1)
14.4.1.4 Control Performance Monitoring Software
213(1)
14.4.2 Management Solutions
214(5)
14.4.2.1 Training of MPC Console Operators
214(1)
14.4.2.2 Training of MPC Control Engineers
215(1)
14.4.2.3 Development of Corporate MPC Standards and Guidelines
216(1)
14.4.2.4 Central Engineering Support Organization for MPC
217(2)
14.4.3 Outsourcing Solutions
219(2)
15 MPC Performance Monitoring 221(14)
15.1 Why Performance Assessment of MPC Application Is Necessary
221(1)
15.2 Types of Performance Assessment
222(1)
15.2.1 Control Performance
222(1)
15.2.2 Optimization Performance
222(1)
15.2.3 Economic Performance
222(1)
15.2.4 Intangible Performance
222(1)
15.3 Benefit Measurement after MPC Implementation
222(1)
15.4 Parameters to Be Monitored for MPC Performance Evaluation
223(5)
15.4.1 Service Factors
224(1)
15.4.2 KPI for Financial Criteria
224(1)
15.4.3 KPI for Standard Deviation of Key Process Variable
225(1)
15.4.3.1 Safety Parameters
225(1)
15.4.3.2 Quality Giveaway Parameters
225(1)
15.4.3.3 Economic Parameters
225(1)
15.4.4 KPI for Constraint Activity
226(1)
15.4.5 KPI for Constraint Violation
226(1)
15.4.6 KPI for Inferential Model Monitoring
226(1)
15.4.7 Model Quality
226(1)
15.4.8 Limit Change Frequencies for CV/MVs
227(1)
15.4.9 Active MV Limit
227(1)
15.4.10 Long-Term Performance Monitoring of MPC
227(1)
15.5 KPIs to Troubleshoot Poor Performance of Multivariable Controls
228(3)
15.5.1 Supporting KPIs for Low Service Factor
228(1)
15.5.2 KPIs to Troubleshoot Cycling
229(1)
15.5.3 KPIs for Oscillation Detection
230(1)
15.5.4 KPIs for Regulatory Control Issues
230(1)
15.5.5 KPIs for Measuring Operator Actions
231(1)
15.5.6 KPIs for Measuring Process Changes and Disturbances
231(1)
15.6 Exploitation of Constraints Handling and Maximization of MPC Benefit
231(4)
16 Commercial MPC Vendors and Applications 235(28)
16.1 Basic Modules and Components of Commercial MPC Software
235(8)
16.1.1 Basic MPC Package
235(1)
16.1.2 Data Collection Module
236(1)
16.1.3 MPC Online Controller
236(1)
16.1.4 Operator/Engineer Station
237(1)
16.1.5 System Identification Module
237(3)
16.1.5.1 Different Modeling Options
239(1)
16.1.5.2 Reporting and Documentation Function
239(1)
16.1.5.3 Data Analysis and Pre-Processing
239(1)
16.1.6 PC-Based Offline Simulation Package
240(1)
16.1.7 Control Performance Monitoring and Diagnostics Software
240(2)
16.1.7.1 Control Performance Monitoring
240(1)
16.1.7.2 Basic Features of Performance Monitoring and Diagnostics Software
240(1)
16.1.7.3 Performance and Benefits Metrics
241(1)
16.1.7.4 Offline Module
241(1)
16.1.7.5 Online Package
241(1)
16.1.7.6 Online Reports
241(1)
16.1.8 Soft Sensor Module (Also Called Quality Estimator Module)
242(2)
16.1.8.1 Soft Sensor Offline Package
242(1)
16.1.8.2 Soft Sensor Online Package
243(1)
16.1.8.3 Soft Sensor Module Simulation Tool
243(1)
16.2 Major Commercial MPC Software
243(1)
16.3 AspenTech and DMCplus
244(7)
16.3.1 Brief History of Development
244(2)
16.3.1.1 Enhancement of DMC Technology to QDMC Technology in 1983, Regarded as Second-Generation of MPC Technology (1980-1985)
244(1)
16.3.1.2 Introduction of AspenTech and Evolvement of Third-Generation MPC Technology (1985-1990)
245(1)
16.3.1.3 Appearance of DMCplus Product with Fourth-Generation MPC Technology (1990-2000)
245(1)
16.3.1.4 Improvement of DMCplus Technology for Quicker Implementation in Shop Floor, Regarded as Fifth-Generation MPC (2000-2015)
245(1)
16.3.2 DMCplus Product Package
246(2)
16.3.2.1 Aspen DMCplus Desktop
246(1)
16.3.2.2 Aspen DMCplus Online
246(1)
16.3.2.3 DMCplus Models and Identification Package
247(1)
16.3.2.4 Aspen IQ (Soft Sensor Software)
247(1)
16.3.2.5 Aspen Watch: AspenTech MPC Monitoring and Diagnostic Software
247(1)
16.3.3 Distinctive Features of DMCplus Software Package
248(3)
16.3.3.1 Automating Best Practices in Process Unit Step Testing
248(1)
16.3.3.2 Adaptive Modeling
248(1)
16.3.3.3 New Innovation
249(1)
16.3.3.4 Background Step Testing
250(1)
16.4 RMPCT by Honeywell
251(2)
16.4.1 Brief History of Development
251(1)
16.4.2 Honeywell MPC Product Package and Its Special Features
251(1)
16.4.3 Key Features and Functions of RMPCT
251(1)
16.4.3.1 Special Feature to Handle Model Error
251(1)
16.4.3.2 Coping with Model Error
252(1)
16.4.3.3 Funnels
252(1)
16.4.3.4 Range Control Algorithm
252(1)
16.4.4 Product Value Optimization Capabilities
252(1)
16.4.5 "One-Knob" Tuning
253(1)
16.5 SMOC-Shell Global Solution
253(8)
16.5.1 Evolution of Advance Process Control in Shell
253(2)
16.5.1.1 1975-1998: The Beginnings
253(1)
16.5.1.2 1998-2008: Shell Global Solution and Partnering with Yokogawa Era
254(1)
16.5.1.3 2008 Onward: Shell Returns to Its Own Application
254(1)
16.5.2 Shell MPC Product Package and Its Special Features
255(1)
16.5.2.1 Key Characteristics of SMOC
255(1)
16.5.2.2 Applications
255(1)
16.5.3 SMOC Integrated Software Modules
255(4)
16.5.3.1 AIDAPro Offline Modeling Package
256(1)
16.5.3.2 MDPro
256(1)
16.5.3.3 RQEPro
256(1)
16.5.3.4 SMOCPro
257(2)
16.5.4 SMOC Claim of Superior Distinctive Features
259(2)
16.5.4.1 Integrated Dynamic Modeling Tools and Automatic Step Tests
259(1)
16.5.4.2 State-of-the-Art Online Commissioning Tools
259(1)
16.5.4.3 Online Tuning
259(1)
16.5.4.4 Advance Regulatory Controls
260(1)
16.5.4.5 Features of New Product
260(1)
16.6 Conclusion
261(2)
Index 263
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.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97811192435192e.html
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