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Automated fault analysis is not widely used within chemical processing industries due to problems of cost and performance as well as the difficulty of modeling process behavior at needed levels of detail. In response, this book presents the method of minimal evidence (MOME), a model-based diagnostic strategy that facilitates the development and implementation of optimal automated process fault analyzers. With this book as their guide, readers have a powerful new tool for ensuring the safety and reliability of any chemical processing system.
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Automated fault analysis is not widely used within chemical processing industries due to problems of cost and performance as well as the difficulty of modeling process behavior at needed levels of detail. In response, this book presents the method of minimal evidence (MOME), a model-based diagnostic strategy that facilitates the development and implementation of optimal automated process fault analyzers. With this book as their guide, readers have a powerful new tool for ensuring the safety and reliability of any chemical processing system.
Produktdetails
- Produktdetails
- Verlag: Wiley & Sons
- 1. Auflage
- Seitenzahl: 224
- Erscheinungstermin: 4. Januar 2013
- Englisch
- Abmessung: 236mm x 160mm x 20mm
- Gewicht: 590g
- ISBN-13: 9781118372319
- ISBN-10: 111837231X
- Artikelnr.: 36265671
- Verlag: Wiley & Sons
- 1. Auflage
- Seitenzahl: 224
- Erscheinungstermin: 4. Januar 2013
- Englisch
- Abmessung: 236mm x 160mm x 20mm
- Gewicht: 590g
- ISBN-13: 9781118372319
- ISBN-10: 111837231X
- Artikelnr.: 36265671
Dr. Richard J. Fickelscherer is currently a licensed Professional Engineer and is a principal owner of FALCONEER Technologies, LLC. He has developed and implemented programs which provide various supervisory control functions for DuPont, Exxon-Mobile, Merck Pharmaceuticals, Koch Industries, the FMC Corporation and many other client companies. Dr. Daniel L. Chester joined the Department of Computer and Information Sciences at the University of Delaware in 1980, where he soon became one of the principal investigators on the FALCON project. He is currently Associate Chair in the computer science department at the University of Delaware. He has been involved in the creation and development of three companies, one of which is FALCONEER Technologies, LLC. He is also co-inventor in five U.S. patents.
Dedication Table of Contents Foreword Preface Acknowledgements Chapter 1.
Motivations for Automating Process Fault Analysis 1.1 Introduction 1.2 CPI
Trends to Date 1.3 The Changing Role for the Process Operators in Plant
Operations 1.4 Methods Currently Used to Perform Process Fault Management
1.5 Limitations of Human Operators in Performing Process Fault Management
1.6 The Role of Automated Process Fault Analysis 1.7 Anticipated Future CPI
Trends 1.8 Process Fault Analysis Concept Terminology Chapter 2. Method of
Minimal Evidence: Model-Based Reasoning 2.1 Overview 2.2 Introduction 2.3
Method of Minimal Evidence Overview 2.4 Verifying the Validity and Accuracy
of the Various Primary Models 2.5 Summary Chapter 3. Method of Minimal
Evidence: Diagnostic Strategy Details 3.1 Overview 3.2 Introduction 3.3
MOME Diagnostic Strategy 3.4 A General Procedure for Developing and
Verifying Competent Model-based 3.5 MOME SV & PFA Diagnostic Logic Compiler
Motivations 3.6 MOME Diagnostic Strategy Summary Chapter 4. Method of
Minimal Evidence: Fuzzy Logic Algorithm 4.1 Overview 4.2 Introduction 4.3
Fuzzy Logic Overview 4.4 MOME Fuzzy Logic Algorithm 4.5 Certainty Factor
Calculation Review 4.6 MOME Fuzzy Logic Algorithm Summary Chapter 5. Method
of Minimal Evidence: Criteria for Shrewdly Distribution Fault Analyzers and
Strategic Process Sensor Placement 5.1 Overview 5.2 Criteria for Shrewdly
Distributing Process Fault Analyzers 5.3 Criteria for Strategic Process
Sensor Placement Chapter 6. Virtual SPC Analysis and Its Routine Use in
Falconeer(TM) IV 6.1 Overview 6.2 Introduction 6.3 EWMA Calculations and
Specific Virtual SPC Analysis Configurations 6.4 Virtual SPC Alarm Trigger
Summary 6.5 Virtual SPC Analysis Conclusions Chapter 7. Process State
Transistion Logic and Its Routine Use in Falconeer(TM) IV 7.1 Temporal
Reasoning Philosophy 7.2 Introduction 7.3 State Identification Analysis
Currently Used in Falconeer(TM) IV 7.4 State Identification Analysis
Summary Chapter 8. Conclusions 8.1 Overview 8.2 Summary of the MOME
Diagnostic Strategy 8.3 FALCON, FALCONEER and FALCONEER(TM) IV Actual KBS
Application Performance Results 8.4 FALCONEER(TM) IV KBS Application
Project Procedure 8.5 Optimal Automated Process Fault Analysis Conclusions
Appendix A. Various Diagnostic Strategies for Automating Process Fault
Analysis Appendix B. The Falcon Project Appendix C. Process State
Transition Logic Used by the Original Falconeer KBS Appendix D.
Falconeer(TM) IV Real-Time Suite Process Performance Solutions Demo
Description
Motivations for Automating Process Fault Analysis 1.1 Introduction 1.2 CPI
Trends to Date 1.3 The Changing Role for the Process Operators in Plant
Operations 1.4 Methods Currently Used to Perform Process Fault Management
1.5 Limitations of Human Operators in Performing Process Fault Management
1.6 The Role of Automated Process Fault Analysis 1.7 Anticipated Future CPI
Trends 1.8 Process Fault Analysis Concept Terminology Chapter 2. Method of
Minimal Evidence: Model-Based Reasoning 2.1 Overview 2.2 Introduction 2.3
Method of Minimal Evidence Overview 2.4 Verifying the Validity and Accuracy
of the Various Primary Models 2.5 Summary Chapter 3. Method of Minimal
Evidence: Diagnostic Strategy Details 3.1 Overview 3.2 Introduction 3.3
MOME Diagnostic Strategy 3.4 A General Procedure for Developing and
Verifying Competent Model-based 3.5 MOME SV & PFA Diagnostic Logic Compiler
Motivations 3.6 MOME Diagnostic Strategy Summary Chapter 4. Method of
Minimal Evidence: Fuzzy Logic Algorithm 4.1 Overview 4.2 Introduction 4.3
Fuzzy Logic Overview 4.4 MOME Fuzzy Logic Algorithm 4.5 Certainty Factor
Calculation Review 4.6 MOME Fuzzy Logic Algorithm Summary Chapter 5. Method
of Minimal Evidence: Criteria for Shrewdly Distribution Fault Analyzers and
Strategic Process Sensor Placement 5.1 Overview 5.2 Criteria for Shrewdly
Distributing Process Fault Analyzers 5.3 Criteria for Strategic Process
Sensor Placement Chapter 6. Virtual SPC Analysis and Its Routine Use in
Falconeer(TM) IV 6.1 Overview 6.2 Introduction 6.3 EWMA Calculations and
Specific Virtual SPC Analysis Configurations 6.4 Virtual SPC Alarm Trigger
Summary 6.5 Virtual SPC Analysis Conclusions Chapter 7. Process State
Transistion Logic and Its Routine Use in Falconeer(TM) IV 7.1 Temporal
Reasoning Philosophy 7.2 Introduction 7.3 State Identification Analysis
Currently Used in Falconeer(TM) IV 7.4 State Identification Analysis
Summary Chapter 8. Conclusions 8.1 Overview 8.2 Summary of the MOME
Diagnostic Strategy 8.3 FALCON, FALCONEER and FALCONEER(TM) IV Actual KBS
Application Performance Results 8.4 FALCONEER(TM) IV KBS Application
Project Procedure 8.5 Optimal Automated Process Fault Analysis Conclusions
Appendix A. Various Diagnostic Strategies for Automating Process Fault
Analysis Appendix B. The Falcon Project Appendix C. Process State
Transition Logic Used by the Original Falconeer KBS Appendix D.
Falconeer(TM) IV Real-Time Suite Process Performance Solutions Demo
Description
Dedication Table of Contents Foreword Preface Acknowledgements Chapter 1.
Motivations for Automating Process Fault Analysis 1.1 Introduction 1.2 CPI
Trends to Date 1.3 The Changing Role for the Process Operators in Plant
Operations 1.4 Methods Currently Used to Perform Process Fault Management
1.5 Limitations of Human Operators in Performing Process Fault Management
1.6 The Role of Automated Process Fault Analysis 1.7 Anticipated Future CPI
Trends 1.8 Process Fault Analysis Concept Terminology Chapter 2. Method of
Minimal Evidence: Model-Based Reasoning 2.1 Overview 2.2 Introduction 2.3
Method of Minimal Evidence Overview 2.4 Verifying the Validity and Accuracy
of the Various Primary Models 2.5 Summary Chapter 3. Method of Minimal
Evidence: Diagnostic Strategy Details 3.1 Overview 3.2 Introduction 3.3
MOME Diagnostic Strategy 3.4 A General Procedure for Developing and
Verifying Competent Model-based 3.5 MOME SV & PFA Diagnostic Logic Compiler
Motivations 3.6 MOME Diagnostic Strategy Summary Chapter 4. Method of
Minimal Evidence: Fuzzy Logic Algorithm 4.1 Overview 4.2 Introduction 4.3
Fuzzy Logic Overview 4.4 MOME Fuzzy Logic Algorithm 4.5 Certainty Factor
Calculation Review 4.6 MOME Fuzzy Logic Algorithm Summary Chapter 5. Method
of Minimal Evidence: Criteria for Shrewdly Distribution Fault Analyzers and
Strategic Process Sensor Placement 5.1 Overview 5.2 Criteria for Shrewdly
Distributing Process Fault Analyzers 5.3 Criteria for Strategic Process
Sensor Placement Chapter 6. Virtual SPC Analysis and Its Routine Use in
Falconeer(TM) IV 6.1 Overview 6.2 Introduction 6.3 EWMA Calculations and
Specific Virtual SPC Analysis Configurations 6.4 Virtual SPC Alarm Trigger
Summary 6.5 Virtual SPC Analysis Conclusions Chapter 7. Process State
Transistion Logic and Its Routine Use in Falconeer(TM) IV 7.1 Temporal
Reasoning Philosophy 7.2 Introduction 7.3 State Identification Analysis
Currently Used in Falconeer(TM) IV 7.4 State Identification Analysis
Summary Chapter 8. Conclusions 8.1 Overview 8.2 Summary of the MOME
Diagnostic Strategy 8.3 FALCON, FALCONEER and FALCONEER(TM) IV Actual KBS
Application Performance Results 8.4 FALCONEER(TM) IV KBS Application
Project Procedure 8.5 Optimal Automated Process Fault Analysis Conclusions
Appendix A. Various Diagnostic Strategies for Automating Process Fault
Analysis Appendix B. The Falcon Project Appendix C. Process State
Transition Logic Used by the Original Falconeer KBS Appendix D.
Falconeer(TM) IV Real-Time Suite Process Performance Solutions Demo
Description
Motivations for Automating Process Fault Analysis 1.1 Introduction 1.2 CPI
Trends to Date 1.3 The Changing Role for the Process Operators in Plant
Operations 1.4 Methods Currently Used to Perform Process Fault Management
1.5 Limitations of Human Operators in Performing Process Fault Management
1.6 The Role of Automated Process Fault Analysis 1.7 Anticipated Future CPI
Trends 1.8 Process Fault Analysis Concept Terminology Chapter 2. Method of
Minimal Evidence: Model-Based Reasoning 2.1 Overview 2.2 Introduction 2.3
Method of Minimal Evidence Overview 2.4 Verifying the Validity and Accuracy
of the Various Primary Models 2.5 Summary Chapter 3. Method of Minimal
Evidence: Diagnostic Strategy Details 3.1 Overview 3.2 Introduction 3.3
MOME Diagnostic Strategy 3.4 A General Procedure for Developing and
Verifying Competent Model-based 3.5 MOME SV & PFA Diagnostic Logic Compiler
Motivations 3.6 MOME Diagnostic Strategy Summary Chapter 4. Method of
Minimal Evidence: Fuzzy Logic Algorithm 4.1 Overview 4.2 Introduction 4.3
Fuzzy Logic Overview 4.4 MOME Fuzzy Logic Algorithm 4.5 Certainty Factor
Calculation Review 4.6 MOME Fuzzy Logic Algorithm Summary Chapter 5. Method
of Minimal Evidence: Criteria for Shrewdly Distribution Fault Analyzers and
Strategic Process Sensor Placement 5.1 Overview 5.2 Criteria for Shrewdly
Distributing Process Fault Analyzers 5.3 Criteria for Strategic Process
Sensor Placement Chapter 6. Virtual SPC Analysis and Its Routine Use in
Falconeer(TM) IV 6.1 Overview 6.2 Introduction 6.3 EWMA Calculations and
Specific Virtual SPC Analysis Configurations 6.4 Virtual SPC Alarm Trigger
Summary 6.5 Virtual SPC Analysis Conclusions Chapter 7. Process State
Transistion Logic and Its Routine Use in Falconeer(TM) IV 7.1 Temporal
Reasoning Philosophy 7.2 Introduction 7.3 State Identification Analysis
Currently Used in Falconeer(TM) IV 7.4 State Identification Analysis
Summary Chapter 8. Conclusions 8.1 Overview 8.2 Summary of the MOME
Diagnostic Strategy 8.3 FALCON, FALCONEER and FALCONEER(TM) IV Actual KBS
Application Performance Results 8.4 FALCONEER(TM) IV KBS Application
Project Procedure 8.5 Optimal Automated Process Fault Analysis Conclusions
Appendix A. Various Diagnostic Strategies for Automating Process Fault
Analysis Appendix B. The Falcon Project Appendix C. Process State
Transition Logic Used by the Original Falconeer KBS Appendix D.
Falconeer(TM) IV Real-Time Suite Process Performance Solutions Demo
Description