LEADER 05390nam 2200661 a 450 001 9910830586303321 005 20170814184447.0 010 $a1-118-48195-X 010 $a1-283-91735-1 010 $a1-118-48193-3 010 $a1-118-48196-8 035 $a(CKB)2670000000308729 035 $a(EBL)1092858 035 $a(OCoLC)823726462 035 $a(SSID)ssj0000785052 035 $a(PQKBManifestationID)11501073 035 $a(PQKBTitleCode)TC0000785052 035 $a(PQKBWorkID)10783393 035 $a(PQKB)10959393 035 $a(OCoLC)823506868 035 $a(MiAaPQ)EBC1092858 035 $a(PPN)174941587 035 $a(EXLCZ)992670000000308729 100 $a20120808d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aOptimal automated process fault analysis$b[electronic resource] /$fRichard J. Fickelscherer ; Daniel L. Chester 210 $aHoboken, N.J. $cJohn Wiley and Sons, Inc.$d2013 215 $a1 online resource (226 p.) 300 $a"AlChE." 311 $a1-118-37231-X 320 $aIncludes bibliographical references and index. 327 $aOptimal Automated Process Fault Analysis; Contents; Foreword; Preface; Acknowledgments; 1 Motivations for Automating Process Fault Analysis; 1.1 Introduction; 1.2 CPI Trends to Date; 1.3 The Changing Role of 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; References; 2 Method of Minimal Evidence: Model-Based Reasoning; 2.1 Overview 327 $a2.2 Introduction2.3 Method of Minimal Evidence Overview; 2.3.1 Process Model and Modeling Assumption Variable Classifications; 2.3.2 Example of a MOME Primary Model; 2.3.3 Example of MOME Secondary Models; 2.3.4 Primary Model Residuals' Normal Distributions; 2.3.5 Minimum Assumption Variable Deviations; 2.3.6 Primary Model Derivation Issues; 2.3.7 Method for Improving the Diagnostic Sensitivity of the Resulting Fault Analyzer; 2.3.8 Intermediate Assumption Deviations, Process Noise, and Process Transients; 2.4 Verifying the Validity and Accuracy of the Various Primary Models; 2.5 Summary 327 $aReferences3 Method of Minimal Evidence: Diagnostic Strategy Details; 3.1 Overview; 3.2 Introduction; 3.3 MOME Diagnostic Strategy; 3.3.1 Example of MOME SV&PFA Diagnostic Rules' Logic; 3.3.2 Example of Key Performance Indicator Validation; 3.3.3 Example of MOME SV&PFA Diagnostic Rules with Measurement Redundancy; 3.3.4 Example of MOME SV&PFA Diagnostic Rules for Interactive Multiple-Faults; 3.4 General Procedure for Developing and Verifying Competent Model-Based Process Fault Analyzers; 3.5 MOME SV&PFA Diagnostic Rules' Logic Compiler Motivations; 3.6 MOME Diagnostic Strategy Summary 327 $aReferences4 Method of Minimal Evidence: Fuzzy Logic Algorithm; 4.1 Overview; 4.2 Introduction; 4.3 Fuzzy Logic Overview; 4.4 MOME Fuzzy Logic Algorithm; 4.4.1 Single-Fault Fuzzy Logic Diagnostic Rule; 4.4.2 Multiple-Fault Fuzzy Logic Diagnostic Rule; 4.5 Certainty Factor Calculation Review; 4.6 MOME Fuzzy Logic Algorithm Summary; References; 5 Method of Minimal Evidence: Criteria for Shrewdly Distributing Fault Analyzers and Strategic Process Sensor Placement; 5.1 Overview; 5.2 Criteria for Shrewdly Distributing Process Fault Analyzers; 5.2.1 Introduction 327 $a5.2.2 Practical Limitations on Target Process System Size5.2.3 Distributed Fault Analyzers; 5.3 Criteria for Strategic Process Sensor Placement; References; 6 Virtual SPC Analysis and Its Routine Use in FALCONEERTM IV; 6.1 Overview; 6.2 Introduction; 6.3 EWMA Calculations and Specific Virtual SPC Analysis Configurations; 6.3.1 Controlled Variables; 6.3.2 Uncontrolled Variables and Performance Equation Variables; 6.4 Virtual SPC Alarm Trigger Summary; 6.5 Virtual SPC Analysis Conclusions; References; 7 Process State Transition Logic and Its Routine Use in FALCONEERTM IV 327 $a7.1 Temporal Reasoning Philosophy 330 $a 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. 606 $aChemical process control$xData processing 606 $aFault location (Engineering)$xData processing 615 0$aChemical process control$xData processing. 615 0$aFault location (Engineering)$xData processing. 676 $a660.2815 676 $a660/.2815 676 $a670 700 $aFickelscherer$b Richard J$01701970 701 $aChester$b Daniel L$01701971 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830586303321 996 $aOptimal automated process fault analysis$94086136 997 $aUNINA