Optimal Automated Process Fault Analysis

Automated fault analysis is still 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.  

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™ 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™ IV 7.1 Temporal Reasoning Philosophy 7.2 Introduction 7.3 State Identification Analysis Currently Used in Falconeer™ 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™ IV Actual KBS Application Performance Results 8.4 FALCONEER™ 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™ IV Real–Time Suite Process Performance Solutions Demo Description