Multivariable Predictive Control

Applications in Industry
 
 
John Wiley & Sons Inc (Verlag)
  • erschienen am 10. August 2017
  • |
  • 304 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-119-24351-9 (ISBN)
 
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.
1. Auflage
  • Englisch
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  • |
  • Großbritannien
  • Für Beruf und Forschung
  • 6,84 MB
978-1-119-24351-9 (9781119243519)
1119243513 (1119243513)
weitere Ausgaben werden ermittelt
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.
  • 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
  • References
  • 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
  • References
  • 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
  • References
  • 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
  • References
  • 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
  • References
  • 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
  • References
  • Chapter 8 Preliminary Process Test and Step Test
  • 8.1 Pre-Stepping, or Preliminary Process Test
  • 8.1.1 What Is Pre-Stepping?
  • 8.1.2 Objective of Pre-Stepping
  • 8.1.3 Prerequisites of Pre-Stepping
  • 8.1.4 Pre-Stepping
  • 8.2 Step Testing
  • 8.2.1 What Is a Step Test?
  • 8.2.2 What Is the Purpose of a Step Test?
  • 8.2.3 Details of Step Testing
  • 8.2.3.1 Administrative Aspects
  • 8.2.3.2 Technical Aspects
  • 8.2.4 Different Step-Testing Method
  • 8.2.4.1 Manual Step Testing
  • 8.2.4.2 PRBS (Pseudo Random Binary Sequence)
  • 8.2.4.3 General Guidelines of PRBS Test
  • 8.2.5 Difference between Normal Step Testing and PRBS Testing
  • 8.2.6 Which One to Choose?
  • 8.2.7 Dos and Don'ts of Step Testing
  • 8.3 Development of Step-Testing Methodology over the Years
  • Reference
  • Chapter 9 Model Building and System Identification
  • 9.1 Introduction to Model Building
  • 9.2 Key Issues in Model Identifications
  • 9.2.1 Identification Test
  • 9.2.2 Model Structure and Parameter Estimation
  • 9.2.3 Order Selection
  • 9.2.4 Model Validation
  • 9.3 The Basic Steps of System Identification
  • 9.3.1 Step 0: Experimental Design and Execution
  • 9.3.2 Step 1: Plan the Case that Needs to Be Modeled
  • 9.3.2.1 Action 1
  • 9.3.2.2 Action 2
  • 9.3.3 Step 2: Identify Good Slices of Data
  • 9.3.3.1 Looking at the Data
  • 9.3.4 Step 3: Pre-Processing of Data
  • 9.3.5 Step 4: Identification of Model Curve
  • 9.3.5.1 Hybrid Approach to System Identification
  • 9.3.5.2 Direct Modeling Approach of System Identification
  • 9.3.5.3 Subspace Identification
  • 9.3.5.4 Detailed Steps of Implementations
  • 9.3.6 Step 5: Select Final Model
  • 9.4 Model Structures
  • 9.4.1 FIR Models
  • 9.4.1.1 FIR Structures
  • 9.4.2 Prediction Error Models (PEM Models)
  • 9.4.2.1 PEM Structures
  • 9.4.3 Model for Order and Variance Reduction
  • 9.4.3.1 ARX Parametric Models (Discrete Time)
  • 9.4.3.2 Output Error Models (Discrete Time)
  • 9.4.3.3 Laplace Domain Parametric Models
  • 9.4.3.4 Final Model Form
  • 9.4.4 State-Space Models
  • 9.4.5 How to Know Which Structure and Method to Use
  • 9.5 Common Features of Commercial Identification Packages
  • References
  • Chapter 10 Soft Sensors
  • 10.1 What Is a Soft Sensor?
  • 10.2 Why Soft Sensors Are Necessary
  • 10.2.1 Process Monitoring and Process Fault Detection
  • 10.2.2 Sensor Fault Detection and Reconstruction
  • 10.2.3 Use of Soft Sensors in MPC Application
  • 10.3 Types of Soft Sensors
  • 10.3.1 First Principle-Based Soft Sensors
  • 10.3.1.1 Advantages
  • 10.3.1.2 Disadvantages
  • 10.3.2 Data-Driven Soft Sensors
  • 10.3.2.1 Advantages
  • 10.3.2.2 Disadvantages
  • 10.3.3 Gray Model-Based Soft Sensors
  • 10.3.3.1 Advantages
  • 10.3.4 Hybrid Model-Based Soft Sensors
  • 10.3.4.1 Advantages
  • 10.4 Soft Sensors Development Methodology
  • 10.4.1 Data Collection and Data Inspection
  • 10.4.2 Data Preprocessing and Data Conditioning
  • 10.4.2.1 Outlier Detection and Replacement
  • 10.4.2.2 Univariate Approach to Detect Outliers
  • 10.4.2.3 Multivariate Approach to Detect Outliers (Lin 2007)
  • 10.4.2.4 Handling of Missing Data
  • 10.4.3 Selection of Relevant Input Output Variables
  • 10.4.4 Data Alignment
  • 10.4.5 Model Selection, Training, and Validation (Kadlec 2009
  • Lin 2007)
  • 10.4.6 Analyze Process Dynamics
  • 10.4.7 Deployment and Maintenance
  • 10.5 Data-Driven Methods for Soft Sensing
  • 10.5.1 Principle Component Analysis
  • 10.5.1.1 The Basics of PCA
  • 10.5.1.2 Why Do We Need to Rotate the Data?
  • 10.5.1.3 How Do We Generate Principal Components?
  • 10.5.1.4 Steps to Calculating Principal Components
  • 10.5.2 Partial Least Squares
  • 10.5.3 Artificial Neural Networks
  • 10.5.3.1 Network Architecture
  • 10.5.3.2 Back Propagation Algorithm (BPA)
  • 10.5.4 Neuro-Fuzzy Systems
  • 10.5.5 Support Vector Machines
  • 10.5.5.1 Support Vector Regression-Based Modeling
  • 10.6 Open Issues and Future Steps of Soft Sensor Development
  • 10.6.1 Large Effort Required for Preprocessing of Industrial Data
  • 10.6.2 Which Modeling Method to Choose?
  • 10.6.3 Agreement of the Developed Model with Physics of the Process
  • 10.6.4 Performance Deterioration of Developed Soft Sensor Model
  • References
  • Chapter 11 Offline Simulation
  • 11.1 What Is Offline Simulation?
  • 11.2 Purpose of Offline Simulation
  • 11.3 Main Task of Offline Simulation
  • 11.4 Understanding Different Tuning Parameters of Offline Simulations
  • 11.4.1 Tuning Parameters for CVs
  • 11.4.1.1 Methods for Handling of Infeasibility
  • 11.4.1.2 Priority Ranking of CVs
  • 11.4.1.3 CV Give-Up
  • 11.4.1.4 CV Error Weight
  • 11.4.2 Tuning Parameters for MVs
  • 11.4.2.1 MV Maximum Movement Limits or Rate-of-Change Limits
  • 11.4.2.2 Movement Weights
  • 11.4.3 Tuning Parameters for Optimizer
  • 11.4.3.1 Economic Optimization
  • 11.4.3.2 General Form of Objective Function
  • 11.4.3.3 Weighting Coefficients
  • 11.4.3.4 Setting Linear Objective Coefficients
  • 11.4.3.5 Optimization Horizon and Optimization Speed Factor
  • 11.4.3.6 Optimization Speed Factor
  • 11.4.3.7 MV Optimization Priority
  • 11.4.4 Soft Limits
  • 11.4.4.1 How Soft Limits Work
  • 11.4.4.2 CV Soft Limits
  • 11.4.4.3 MV Soft Limits
  • 11.5 Different Steps to Build and Activate Simulator in an Offline PC
  • 11.6 Example of Tests Carried out in Simulator
  • 11.6.1 Control and Optimization Objectives
  • 11.6.1.1 Test 1
  • 11.6.1.2 Test 2
  • 11.6.1.3 Test 3
  • 11.6.1.4 Test 4
  • 11.6.1.5 Test 5
  • 11.6.1.6 Test 6
  • 11.6.1.7 Others Tests
  • 11.7 Guidelines for Choosing Tuning Parameters
  • 11.7.1 Guidelines for Choosing Initial Values
  • 11.7.2 How to Select Maximum Move Size and MV Movement Weights During Simulation Study
  • References
  • Chapter 12 Online Deployment of MPC Application in Real Plants
  • 12.1 What Is Online Deployment (Controller Commissioning)?
  • 12.2 Steps for Controller Commissioning
  • 12.2.1 Set up the Controller Configuration and Final Review of the Model
  • 12.2.2 Build the Controller
  • 12.2.3 Load Operator Station on PC Near the Panel Operator
  • 12.2.4 Take MPC Controller in Line with Prediction Mode
  • 12.2.5 Put the MPC Controller in Close Loop with One CV at a Time
  • 12.2.6 Observe MPC Controller Performance
  • 12.2.7 Put Optimizer in Line and Observe Optimizer Performance
  • 12.2.8 Evaluate Overall Controller Performance
  • 12.2.9 Perform Online Tuning and Troubleshooting
  • 12.2.10 Train Operators and Engineers on Online Platform
  • 12.2.11 Document MPC Features
  • 12.2.12 Maintain the MPC Controller
  • References
  • Chapter 13 Online Controller Tuning
  • 13.1 What Is Online MPC Controller Tuning?
  • 13.2 Basics of Online Tuning
  • 13.2.1 Key Checkout Regarding Controller Performance
  • 13.2.2 Steps to Troubleshoot the Problem
  • 13.3 Guidelines to Choose Different Tuning Parameters
  • References
  • Chapter 14 Why Do Some MPC Applications Fail?
  • 14.1 What Went Wrong?
  • 14.2 Failure to Build Efficient MPC Application
  • 14.2.1 Historical Perspective
  • 14.2.2 Capability of MPC Software to Capture Benefits
  • 14.2.3 Expertise of Implementation Team
  • 14.2.3.1 MPC Vendor Limitations
  • 14.2.3.2 Client Limitations
  • 14.2.4 Reliability of APC Project Methodology
  • 14.3 Contributing Failure Factors of Postimplementation MPC Application
  • 14.3.1 Technical Failure Factors
  • 14.3.1.1 Lack of Performance Monitoring of MPC Application
  • 14.3.1.2 Unresolved Basic Control Problems
  • 14.3.1.3 Poor Tuning and Degraded Model Quality
  • 14.3.1.4 Problems Related to Controller Design
  • 14.3.1.5 Significant Process Modifications and Enhancement
  • 14.3.2 Nontechnical Failure Factors
  • 14.3.2.1 Lack of Properly Trained Personnel
  • 14.3.2.2 Lack of Standards and Guidelines to MPC Support Personnel
  • 14.3.2.3 Lack of Organizational Collaboration and Alignment
  • 14.3.2.4 Poor Management of Control System
  • 14.4 Strategies to Avoid MPC Failures
  • 14.4.1 Technical Solutions
  • 14.4.1.1 Development of Online Performance Monitoring of APC Applications
  • 14.4.1.2 Improvement of Base Control Layer
  • 14.4.1.3 Tuning Basic Controls
  • 14.4.1.4 Control Performance Monitoring Software
  • 14.4.2 Management Solutions
  • 14.4.2.1 Training of MPC Console Operators
  • 14.4.2.2 Training of MPC Control Engineers
  • 14.4.2.3 Development of Corporate MPC Standards and Guidelines
  • 14.4.2.4 Central Engineering Support Organization for MPC
  • 14.4.3 Outsourcing Solutions
  • References
  • Chapter 15 MPC Performance Monitoring
  • 15.1 Why Performance Assessment of MPC Application Is Necessary
  • 15.2 Types of Performance Assessment
  • 15.2.1 Control Performance
  • 15.2.2 Optimization Performance
  • 15.2.3 Economic Performance
  • 15.2.4 Intangible Performance
  • 15.3 Benefit Measurement after MPC Implementation
  • 15.4 Parameters to Be Monitored for MPC Performance Evaluation
  • 15.4.1 Service Factors
  • 15.4.2 KPI for Financial Criteria
  • 15.4.3 KPI for Standard Deviation of Key Process Variable
  • 15.4.3.1 Safety Parameters
  • 15.4.3.2 Quality Giveaway Parameters
  • 15.4.3.3 Economic Parameters
  • 15.4.4 KPI for Constraint Activity
  • 15.4.5 KPI for Constraint Violation
  • 15.4.6 KPI for Inferential Model Monitoring
  • 15.4.7 Model Quality
  • 15.4.8 Limit Change Frequencies for CV/MVs
  • 15.4.9 Active MV Limit
  • 15.4.10 Long-Term Performance Monitoring of MPC
  • 15.5 KPIs to Troubleshoot Poor Performance of Multivariable Controls
  • 15.5.1 Supporting KPIs for Low Service Factor
  • 15.5.2 KPIs to Troubleshoot Cycling
  • 15.5.3 KPIs for Oscillation Detection
  • 15.5.4 KPIs for Regulatory Control Issues
  • 15.5.5 KPIs for Measuring Operator Actions
  • 15.5.6 KPIs for Measuring Process Changes and Disturbances
  • 15.6 Exploitation of Constraints Handling and Maximization of MPC Benefit
  • References
  • Chapter 16 Commercial MPC Vendors and Applications
  • 16.1 Basic Modules and Components of Commercial MPC Software
  • 16.1.1 Basic MPC Package
  • 16.1.2 Data Collection Module
  • 16.1.3 MPC Online Controller
  • 16.1.4 Operator/ Engineer Station
  • 16.1.5 System Identification Module
  • 16.1.5.1 Different Modeling Options
  • 16.1.5.2 Reporting and Documentation Function
  • 16.1.5.3 Data Analysis and Pre-Processing
  • 16.1.6 PC-Based Offline Simulation Package
  • 16.1.7 Control Performance Monitoring and Diagnostics Software
  • 16.1.7.1 Control Performance Monitoring
  • 16.1.7.2 Basic Features of Performance Monitoring and Diagnostics Software
  • 16.1.7.3 Performance and Benefits Metrics
  • 16.1.7.4 Offline Module
  • 16.1.7.5 Online Package
  • 16.1.7.6 Online Reports
  • 16.1.8 Soft Sensor Module (Also Called Quality Estimator Module)
  • 16.1.8.1 Soft Sensor Offline Package
  • 16.1.8.2 Soft Sensor Online Package
  • 16.1.8.3 Soft Sensor Module Simulation Tool
  • 16.2 Major Commercial MPC Software
  • 16.3 AspenTech and DMCplus
  • 16.3.1 Brief History of Development
  • 16.3.1.1 Enhancement of DMC Technology to QDMC Technology in 1983, Regarded as Second-Generation of MPC Technology (1980-1985)
  • 16.3.1.2 Introduction of AspenTech and Evolvement of Third-Generation MPC Technology (1985-1990)
  • 16.3.1.3 Appearance of DMCplus Product with Fourth-Generation MPC Technology (1990-2000)
  • 16.3.1.4 Improvement of DMCplus Technology for Quicker Implementation in Shop Floor, Regarded as Fifth-Generation MPC (2000-2015)
  • 16.3.2 DMCplus Product Package
  • 16.3.2.1 Aspen DMCplus Desktop
  • 16.3.2.2 Aspen DMCplus Online
  • 16.3.2.3 DMCplus Models and Identification Package
  • 16.3.2.4 Aspen IQ (Soft Sensor Software)
  • 16.3.2.5 Aspen Watch: AspenTech MPC Monitoring and Diagnostic Software
  • 16.3.3 Distinctive Features of DMCplus Software Package
  • 16.3.3.1 Automating Best Practices in Process Unit Step Testing
  • 16.3.3.2 Adaptive Modeling
  • 16.3.3.3 New Innovation
  • 16.3.3.4 Background Step Testing
  • 16.4 RMPCT by Honeywell
  • 16.4.1 Brief History of Development
  • 16.4.2 Honeywell MPC Product Package and Its Special Features
  • 16.4.3 Key Features and Functions of RMPCT
  • 16.4.3.1 Special Feature to Handle Model Error
  • 16.4.3.2 Coping with Model Error
  • 16.4.3.3 Funnels
  • 16.4.3.4 Range Control Algorithm
  • 16.4.4 Product Value Optimization Capabilities
  • 16.4.5 "One-Knob" Tuning
  • 16.5 SMOC-Shell Global Solution
  • 16.5.1 Evolution of Advance Process Control in Shell
  • 16.5.1.1 1975-1998: The Beginnings
  • 16.5.1.2 1998-2008: Shell Global Solution and Partnering with Yokogawa Era
  • 16.5.1.3 2008 Onward: Shell Returns to Its Own Application
  • 16.5.2 Shell MPC Product Package and Its Special Features
  • 16.5.2.1 Key Characteristics of SMOC
  • 16.5.2.2 Applications
  • 16.5.3 SMOC Integrated Software Modules
  • 16.5.3.1 AIDAPro Offline Modeling Package
  • 16.5.3.2 MDPro
  • 16.5.3.3 RQEPro
  • 16.5.3.4 SMOCPro
  • 16.5.4 SMOC Claim of Superior Distinctive Features
  • 16.5.4.1 Integrated Dynamic Modeling Tools and Automatic Step Tests
  • 16.5.4.2 State-of-the-Art Online Commissioning Tools
  • 16.5.4.3 Online Tuning
  • 16.5.4.4 Advance Regulatory Controls
  • 16.5.4.5 Features of New Product
  • 16.6 Conclusion
  • References
  • Index
  • EULA

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