LEADER 05381nam 2200649Ia 450 001 9911006596603321 005 20200520144314.0 010 $a1-61583-657-8 010 $a1-281-03520-3 010 $a9786611035204 010 $a0-08-050371-3 035 $a(CKB)1000000000344080 035 $a(EBL)313613 035 $a(OCoLC)476102756 035 $a(SSID)ssj0000135055 035 $a(PQKBManifestationID)11143446 035 $a(PQKBTitleCode)TC0000135055 035 $a(PQKBWorkID)10057695 035 $a(PQKB)10852507 035 $a(MiAaPQ)EBC313613 035 $a(EXLCZ)991000000000344080 100 $a19990806d2000 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData reconciliation & gross error detection $ean intelligent use of process data /$fShankar Narasimhan and Cornelius Jordache 210 $aHouston, TX $cGulf Pub. Co.$dc2000 215 $a1 online resource (425 p.) 300 $aDescription based upon print version of record. 311 $a0-88415-255-3 320 $aIncludes bibliographical references and indexes. 327 $aFront Cover; Data Reconciliation & Gross Error Detection; Copyright Page; Contents; Acknowledgments; Preface; Chapter 1. The Importance of Data Reconciliation and Gross Error Detection; Process Data Conditioning Methods; Industrial Examples of Steady-State Data Reconciliation; Data Reconciliation Problem Formulation; Examples of Simple Reconciliation Problems; Benefits from Data Reconciliation and Gross Error Detection; A Brief History of Data Reconciliation and Gross Error Detection; Scope and Organization of the Book; Summary; References 327 $aChapter 2. Measurement Errors and Error Reduction TechniquesClassification of Measurements Errors; Error Reduction Methods; Summary; References; Chapter 3. Linear Steady-State Data Reconciliation; Linear Systems With All Variables Measured; Linear Systems With Both Measured and Unmeasured Variables; Estimating Measurement Error Covariance Matrix; Simulation Technique for Evaluating Data Reconciliation; Summary; References; Chapter 4. Steady-State Data Reconciliation for Bilinear Systems; Bilinear Systems; Data Reconciliation of Bilinear Systems 327 $aBilinear Data Reconciliation Solution TechniquesSummary; References; Chapter 5. Nonlinear Steady-State Data Reconciliation,; Formulation of Nonlinear Data Reconciliation Problems; Solution Techniques for Equality Constrained Problems; Nonlinear Programming (NLP) Methods for Inequality Constrained; Variable Classification for Nonlinear Data Reconciliation; Comparison of Nonlinear Optimization Strategies for Data Reconciliation; Summary; References; Chapter 6. Data Reconciliation in Dynamic Systems; The Need for Dynamic Data Reconciliation; Linear Discrete Dynamic System Model 327 $aOptimal State Estimation Using Kalman FilterDynamic Data Reconciliation of Nonlinear Systems; Summary; References; Chapter 7. Introduction to Gross Error Detection; Problem Statements; Basic Statistical Tests for Gross Error Detection; Gross Error Detection Using Principal Component (PC) Tests; Statistical Tests for General Steady-State Models; Techniques for Single Gross Error Identification; Detectability and Identifiability of Gross Errors; Proposed Problems; Summary; References; Chapter 8. Multiple Gross Error Identification Strategies for Steady-State Processes 327 $aStrategies for Multiple Gross Error Identification in Linear ProcessesPerformance Measures for Evaluating Gross Error Identification Strategies; Comparison of Multiple Gross Error Identification Strategies; Gross Error Detection in Nonlinear Processes; Bayesian Approach to Multiple Gross Error Identification; Proposed Problems; Summary; References; Chapter 9. Gross Error Detection in Linear Dynamic Systems; Problem Formulation for Detection of Measurement Biases; Statistical Properties of Innovations and the Global Test; Generalized Likelihood Ratio Method; Fault Diagnosis Techniques 327 $aThe State of the Art 330 $aThis book provides a systematic and comprehensive treatment of the variety of methods available for applying data reconciliation techniques. Data filtering, data compression and the impact of measurement selection on data reconciliation are also exhaustively explained.Data errors can cause big problems in any process plant or refinery. Process measurements can be correupted by power supply flucutations, network transmission and signla conversion noise, analog input filtering, changes in ambient conditions, instrument malfunctioning, miscalibration, and the wear and corrosion of sen 517 3 $aData reconciliation and gross error detection 606 $aChemical process control$xAutomation 606 $aAutomatic data collection systems 606 $aError analysis (Mathematics) 615 0$aChemical process control$xAutomation. 615 0$aAutomatic data collection systems. 615 0$aError analysis (Mathematics) 676 $a660/.2815 700 $aNarasimhan$b Shankar$01824849 701 $aJordache$b Cornelius$01824850 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911006596603321 996 $aData reconciliation & gross error detection$94392230 997 $aUNINA