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[null,"1.","Record Nr.",null,"UNINA9911006596603321"]
[null,null,"Autore",null,"Narasimhan Shankar"]
[null,null,"Titolo",null,"Data reconciliation & gross error detection : an intelligent use of process data / / Shankar Narasimhan and Cornelius Jordache"]
[null,null,"Pubbl/distr/stampa",null,"Houston, TX, : Gulf Pub. Co., c2000"]
[null,null,"ISBN",null,"1-61583-657-8"]
[{"$nulls":4},"1-281-03520-3"]
[{"$nulls":4},"9786611035204"]
[{"$nulls":4},"0-08-050371-3"]
[null,null,"Descrizione fisica",null,"1 online resource (425 p.)"]
[null,null,"Disciplina",null,"660/.2815"]
[null,null,"Soggetti",null,"Chemical process control - Automation"]
[{"$nulls":4},"Automatic data collection systems"]
[{"$nulls":4},"Error analysis (Mathematics)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and indexes."]
[null,null,"Nota di contenuto",null,"Front Cover; Data Reconciliation & Gross Error Detection; Copyright Page; Contents; Acknowledgments; Preface; Chapter 1. The Importance of Data Reconciliation and Gross Error Detection; Process Data Conditioning Methods; Industrial Examples of Steady-State Data Reconciliation; Data Reconciliation Problem Formulation; Examples of Simple Reconciliation Problems; Benefits from Data Reconciliation and Gross Error Detection; A Brief History of Data Reconciliation and Gross Error Detection; Scope and Organization of the Book; Summary; References"]
[{"$nulls":4},"Chapter 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"]
[{"$nulls":4},"Bilinear Data Reconciliation Solution TechniquesSummary; References; Chapter 5. Nonlinear Steady-State Data Reconciliation,; Formulation of Nonlinear Data Reconciliation Problems; Solution Techniques for "]
[{"$nulls":4},"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"]
[{"$nulls":4},"Optimal State Estimation Using Kalman FilterDynamic Data Reconciliation of Nonlinear Systems; Summary; References; Chapter 7. Introduction to Gross Error Detection; Problem Statements; Basic Statistical Tests for Gross Error Detection; Gross Error Detection Using Principal Component (PC) Tests; Statistical Tests for General Steady-State Models; Techniques for Single Gross Error Identification; Detectability and Identifiability of Gross Errors; Proposed Problems; Summary; References; Chapter 8. Multiple Gross Error Identification Strategies for Steady-State Processes"]
[{"$nulls":4},"Strategies 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"]
[{"$nulls":4},"The State of the Art"]
[null,null,null,"Sommario/riassunto","This 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"]