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Optimal Automated Process Fault Analysis [Hardback]

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  • Formāts: Hardback, 224 pages, height x width x depth: 235x163x200 mm, weight: 505 g
  • Izdošanas datums: 01-Feb-2013
  • Izdevniecība: Wiley-AIChE
  • ISBN-10: 111837231X
  • ISBN-13: 9781118372319
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Optimal Automated Process Fault AnalysisPalielināt
  • Formāts: Hardback, 224 pages, height x width x depth: 235x163x200 mm, weight: 505 g
  • Izdošanas datums: 01-Feb-2013
  • Izdevniecība: Wiley-AIChE
  • ISBN-10: 111837231X
  • ISBN-13: 9781118372319
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781118372319.html
Keywords:
Tested and proven strategy to develop optimal automated process fault analyzers

Process fault analyzers monitor process operations in order to identify the underlying causes of operational problems. Several diagnostic strategies exist for automating process fault analysis; however, automated fault analysis is still not widely used within the 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. MOME was created at the University of Delaware by the researchers who developed the FALCON system, a real-time, online process fault analyzer. The authors demonstrate how MOME is used to diagnose single and multiple fault situations, determine the strategic placement of process sensors, and distribute fault analyzers within large processing systems.

Optimal Automated Process Fault Analysis begins by exploring the need to automate process fault analysis. Next, the book examines:





Logic of model-based reasoning as used in MOME MOME logic for performing single and multiple fault diagnoses Fuzzy logic algorithms for automating MOME Distributing process fault analyzers throughout large processing systems Virtual SPC analysis and its use in FALCONEER™ IV Process state transition logic and its use in FALCONEER™ IV

The book concludes with a summary of the lessons learned by employing FALCONEER™ IV in actual process applications, including the benefits of "intelligent supervision" of process operations.

With this book as their guide, readers have a powerful new tool for ensuring the safety and reliability of any chemical processing system.
Foreword xiii
Preface xv
Acknowledgments xix
1 Motivations for Automating Process Fault Analysis
1(20)
1.1 Introduction
1(1)
1.2 CPI Trends to Date
1(2)
1.3 The Changing Role of Process Operators in Plant Operations
3(2)
1.4 Methods Currently Used to Perform Process Fault Management
5(5)
1.5 Limitations of Human Operators in Performing Process Fault Management
10(2)
1.6 The Role of Automated Process Fault Analysis
12(1)
1.7 Anticipated Future CPI Trends
13(1)
1.8 Process Fault Analysis Concept Terminology
14(7)
References
16(5)
2 Method of Minimal Evidence: Model-Based Reasoning
21(34)
2.1 Overview
21(1)
2.2 Introduction
22(1)
2.3 Method of Minimal Evidence Overview
23(26)
2.3.1 Process Model and Modeling Assumption Variable Classifications
28(3)
2.3.2 Example of a MOME Primary Model
31(5)
2.3.3 Example of MOME Secondary Models
36(3)
2.3.4 Primary Model Residuals' Normal Distributions
39(2)
2.3.5 Minimum Assumption Variable Deviations
41(3)
2.3.6 Primary Model Derivation Issues
44(3)
2.3.7 Method for Improving the Diagnostic Sensitivity of the Resulting Fault Analyzer
47(1)
2.3.8 Intermediate Assumption Deviations, Process Noise, and Process Transients
48(1)
2.4 Verifying the Validity and Accuracy of the Various Primary Models
49(2)
2.5 Summary
51(4)
References
52(3)
3 Method of Minimal Evidence: Diagnostic Strategy Details
55(32)
3.1 Overview
55(1)
3.2 Introduction
56(1)
3.3 MOME Diagnostic Strategy
57(22)
3.3.1 Example of MOME SV&PFA Diagnostic Rules' Logic
57(10)
3.3.2 Example of Key Performance Indicator Validation
67(4)
3.3.3 Example of MOME SV&PFA Diagnostic Rules with Measurement Redundancy
71(3)
3.3.4 Example of MOME SV&PFA Diagnostic Rules for Interactive Multiple-Faults
74(5)
3.4 General Procedure for Developing and Verifying Competent Model-Based Process Fault Analyzers
79(1)
3.5 MOME SV&PFA Diagnostic Rules' Logic Compiler Motivations
80(3)
3.6 MOME Diagnostic Strategy Summary
83(4)
References
84(3)
4 Method of Minimal Evidence: Fuzzy Logic Algorithm
87(22)
4.1 Overview
87(1)
4.2 Introduction
88(2)
4.3 Fuzzy Logic Overview
90(1)
4.4 MOME Fuzzy Logic Algorithm
91(11)
4.4.1 Single-Fault Fuzzy Logic Diagnostic Rule
93(4)
4.4.2 Multiple-Fault Fuzzy Logic Diagnostic Rule
97(5)
4.5 Certainty Factor Calculation Review
102(2)
4.6 MOME Fuzzy Logic Algorithm Summary
104(5)
References
105(4)
5 Method of Minimal Evidence: Criteria for Shrewdly Distributing Fault Analyzers and Strategic Process Sensor Placement
109(8)
5.1 Overview
109(1)
5.2 Criteria for Shrewdly Distributing Process Fault Analyzers
109(4)
5.2.1 Introduction
110(1)
5.2.2 Practical Limitations on Target Process System Size
110(2)
5.2.3 Distributed Fault Analyzers
112(1)
5.3 Criteria for Strategic Process Sensor Placement
113(4)
References
114(3)
6 Virtual SPC Analysis and Its Routine Use in FALCONEER™ IV
117(8)
6.1 Overview
117(1)
6.2 Introduction
118(1)
6.3 EWMA Calculations and Specific Virtual SPC Analysis Configurations
118(5)
6.3.1 Controlled Variables
119(1)
6.3.2 Uncontrolled Variables and Performance Equation Variables
120(3)
6.4 Virtual SPC Alarm Trigger Summary
123(1)
6.5 Virtual SPC Analysis Conclusions
124(1)
References
124(1)
7 Process State Transition Logic and Its Routine Use in FALCONEER™ IV
125(8)
7.1 Temporal Reasoning Philosophy
125(1)
7.2 Introduction
126(2)
7.3 State Identification Analysis Currently Used in FALCONEER™ IV
128(3)
7.4 State Identification Analysis Summary
131(2)
References
131(2)
8 Conclusions
133(8)
8.1 Overview
133(1)
8.2 Summary of the MOME Diagnostic Strategy
133(1)
8.3 FALCON, FALCONEER, and FALCONEER™ IV
Actual KBS Application Performance Results
134(2)
8.4 FALCONEER™ IV KBS Application Project Procedure
136(2)
8.5 Optimal Automated Process Fault Analysis Conclusions
138(3)
References
139(2)
Appendix A Various Diagnostic Strategies for Automating Process Fault Analysis
141(22)
A.1 Introduction
141(1)
A.2 Fault Tree Analysis
142(1)
A.3 Alarm Analysis
143(1)
A.4 Decision Tables
143(1)
A.5 Sign-Directed Graphs
144(1)
A.6 Diagnostic Strategies Based on Qualitative Models
145(1)
A.7 Diagnostic Strategies Based on Quantitative Models
145(2)
A.8 Artificial Neural Network Strategies
147(1)
A.9 Knowledge-Based System Strategies
147(1)
A.10 Methodology Choice Conclusions
148(15)
References
149(14)
Appendix B The FALCON Project
163(24)
B.1 Introduction
163(1)
B.2 Overview
164(1)
B.3 The Diagnostic Philosophy Underlying the FALCON System
164(1)
B.4 Target Process System
165(2)
B.5 The FALCON System
167(6)
B.5.1 The Inference Engine
168(1)
B.5.2 The Human-Machine Inference
169(1)
B.5.3 The Dynamic Simulation Model
169(3)
B.5.4 The Diagnostic Knowledge Base
172(1)
B.6 Derivation of the FALCON Diagnostic Knowledge
Base
173(1)
B.6.1 First Rapid Prototype of the FALCON
System KBS
173(1)
B.6.2 FALCON System Development
173(9)
B.6.3 The FALCON System's Performance Results
182(1)
B.7 The Ideal FALCON System
183(1)
B.8 Use of the Knowledge-Based System Paradigm in Problem Solving
184(3)
References
185(2)
Appendix C Process State Transition Logic Used by the Original FALCONEER KBS
187(10)
C.1 Introduction
187(1)
C.2 Possible Process Operating States
187(2)
C.3 Significance of Process State Identification and Transition Detection
189(1)
C.4 Methodology for Determining Process State Identification
189(2)
C.4.1 Present-Value States of All Key Sensor Data
189(1)
C.4.2 Predicted Next-Value States of All Key Sensor Data
190(1)
C.5 Process State Identification and Transition Logic Pseudocode
191(5)
C.5.1 Attributes of the Current Data Vector
191(1)
C.5.2 Method Applied to Each Data Vector
192(4)
C.6 Summary
196(1)
Appendix D FALCONEER™ IV Real-Time Suite Process Performance Solutions Demos
197(6)
D.1 FALCONEER™ IV Demos Overview
197(1)
D.2 FALCONEER™ IV Demos
197(6)
D.2.1 Wastewater Treatment Process Demo
197(2)
D.2.2 Pulp and Paper Stock Chest Demo
199(4)
Index 203
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.