Data Reconciliation and Gross Error Detection: An Intelligent Use of Process Data
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Table of contents
- Cover
- Contentsvii
- Acknowledgmentsxiii
- Prefacexv
- Chapter 1. The Importance of Data Reconciliation and Gross Error Detection1
- Process Data Conditioning Methods1
- Industrial Examples of Steady-State Data Reconciliation5
- Data Reconciliation Problem Formulation7
- Examples of Simple Reconciliation Problems11
- Benefits from Data Reconciliation and Gross Error Detection20
- A Brief History of Data Reconciliation and Gross Error Detection21
- Scope and Organization of the Book24
- Summary27
- References28
- Chapter 2. Measurement Errors and Error Reduction Techniques32
- Classification of Measurements Errors32
- Error Reduction Methods38
- Summary56
- References57
- Chapter 3. Linear Steady-State Data Reconciliation59
- Linear Systems With All Variables Measured59
- Linear Systems With Both Measured and Unmeasured Variables63
- Estimating Measurement Error Covariance Matrix77
- Simulation Technique for Evaluating Data Reconciliation81
- Summary82
- References83
- Chapter 4. Steady-State Data Reconciliation for Bilinear Systems85
- Bilinear Systems85
- Data Reconciliation of Bilinear Systems86
- Bilinear Data Reconciliation Solution Techniques97
- Summary117
- References117
- Chapter 5. Nonlinear Steady-State Data Reconciliation,119
- Formulation of Nonlinear Data Reconciliation Problems120
- Solution Techniques for Equality Constrained Problems122
- Nonlinear Programming (NLP) Methods for Inequality Constrained128
- Variable Classification for Nonlinear Data Reconciliation134
- Comparison of Nonlinear Optimization Strategies for Data Reconciliation136
- Summary138
- References138
- Chapter 6. Data Reconciliation in Dynamic Systems142
- The Need for Dynamic Data Reconciliation142
- Linear Discrete Dynamic System Model143
- Optimal State Estimation Using Kalman Filter148
- Dynamic Data Reconciliation of Nonlinear Systems160
- Summary171
- References171
- Chapter 7. Introduction to Gross Error Detection174
- Problem Statements174
- Basic Statistical Tests for Gross Error Detection175
- Gross Error Detection Using Principal Component (PC) Tests195
- Statistical Tests for General Steady-State Models200
- Techniques for Single Gross Error Identification203
- Detectability and Identifiability of Gross Errors209
- Proposed Problems217
- Summary223
- References224
- Chapter 8. Multiple Gross Error Identification Strategies for Steady-State Processes226
- Strategies for Multiple Gross Error Identification in Linear Processes227
- Performance Measures for Evaluating Gross Error Identification Strategies256
- Comparison of Multiple Gross Error Identification Strategies259
- Gross Error Detection in Nonlinear Processes260
- Bayesian Approach to Multiple Gross Error Identification264
- Proposed Problems274
- Summary277
- References278
- Chapter 9. Gross Error Detection in Linear Dynamic Systems281
- Problem Formulation for Detection of Measurement Biases282
- Statistical Properties of Innovations and the Global Test283
- Generalized Likelihood Ratio Method289
- Fault Diagnosis Techniques295
- The State of the Art297
- Summary298
- References298
- Chapter 10. Design of Sensor Networks300
- Estimation Accuracy of Data Reconciliation301
- Sensor Network Design303
- Developments in Sensor Network Design323
- Summary325
- References325
- Chapter 11. Industrial Applications of Data Reconciliation and Gross Error Detection Technologies327
- Process Unit Balance Reconciliation and Gross Error Detection328
- Parameter Estimation and Data Reconciliation331
- Plant-Wide Material and Utilities Reconciliation332
- Case Studies335
- Summary369
- References370
- Appendix A. Basic Concepts of Linear Algebra373
- Appendix B. Graph Theory Fundamentals378
- Appendix C. Fundamentals of Probability and Statistics384
- Index394
- Author Index403
- The Authors406
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 sensors, among other factors. Here's a book that helps you detect, analyze, solve, and avoid the data acquisition problems that can rob plants of peak performance. This indispensable volume provides crucial insights into data reconciliation and gorss error detection techniques that are essential fro optimal process control and information systems.
This book is an invaluable tool for engineers and managers faced with the selection and implementation of data reconciliation software, or for those developing such software. For industrial personnel and students, Data Reconciliation and Gross Error Detection is the ultimate reference.
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 sensors, among other factors. Here's a book that helps you detect, analyze, solve, and avoid the data acquisition problems that can rob plants of peak performance. This indispensable volume provides crucial insights into data reconciliation and gorss error detection techniques that are essential fro optimal process control and information systems.
This book is an invaluable tool for engineers and managers faced with the selection and implementation of data reconciliation software, or for those developing such software. For industrial personnel and students, Data Reconciliation and Gross Error Detection is the ultimate reference.