Elsevier S & T

Data Processing and Reconciliation for Chemical Process Operations

Romagnoli, José A.; Sanchez, Mabel Cristina

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Table of contents
  • Cover
  • CONTENTSvii
  • PREFACExiii
  • ACKNOWLEDGMENTSxv
  • Chapter 1. General Introduction1
  • 1.1. Reliable and Complete Process Knowledge2
  • 1.2. Some Issues Associated with a General Data Reconciliation Problem5
  • 1.3. About This Book6
  • References7
  • Chapter 2. Estimability and Redundancy within the Framework of the General Estimation Theory9
  • 2.1. Introduction9
  • 2.2. Basic Concepts and Definitions10
  • 2.3. Decomposition of the General Estimation Problem14
  • 2.4. Structural Analysis18
  • 2.5. Conclusions19
  • Notation20
  • References20
  • Appendix A21
  • Chapter 3. Classification of the Process Variables for Chemical Plants25
  • 3.1. Introduction25
  • 3.2. Modeling Aspects26
  • 3.3. Classification of Process Variables28
  • 3.4. Analysis of the Process Topology29
  • 3.5. Different Approaches for Solving the Classification Problem32
  • 3.6. Use of Output Set Assignments for Variable Classification35
  • 3.7. The Solution of Special Problems39
  • 3.8. A Complete Classification Example40
  • 3.9. Formulation of a Reduced Reconciliation Problem41
  • 3.10. Conclusions42
  • Notation43
  • References44
  • Appendix A44
  • Appendix B47
  • Chapter 4. Decomposition Using Orthogonal Transformations53
  • 4.1. Introduction53
  • 4.2. Linear Mass Balances54
  • 4.3. Bilinear Multicomponent and Energy Balances62
  • 4.4. Conclusions71
  • Notation71
  • References72
  • Chapter 5. Steady-State Data Reconciliation75
  • 5.1. Introduction75
  • 5.2. Problem Formulation76
  • 5.3. Linear Data Reconciliation77
  • 5.4. Nonlinear Data Reconciliation82
  • 5.5. Conclusions90
  • Notation90
  • References91
  • Appendix A91
  • Chapter 6. Sequential Processing of Information93
  • 6.1. Introduction93
  • 6.2. Sequential Processing of Constraints93
  • 6.3. Sequential Processing of Measurements97
  • 6.4. Alternative Formulation from Estimation Theory99
  • 6.5. Conclusions105
  • Notation105
  • References106
  • Appendix A106
  • Chapter 7. Treatment of Gross Errors109
  • 7.1. Introduction109
  • 7.2. Gross Error Detection111
  • 7.3. Identification of the Measurements with Gross Error114
  • 7.4. Estimation of the Magnitude of Bias and Leaks121
  • 7.5. A Recursive Scheme for Gross Error Identification and Estimation125
  • 7.6. Conclusions129
  • Notation130
  • References131
  • Appendix A132
  • Appendix B133
  • Chapter 8. Rectification of Process Measurement Data in Dynamic Situations137
  • 8.1. Introduction137
  • 8.2. Dynamic Data Reconciliation: A Filtering Approach138
  • 8.3. Dynamic Data Reconciliation: Using Nonlinear Programming Techniques148
  • 8.4. Conclusions155
  • Notation156
  • References157
  • Chapter 9. Joint Parameter Estimation–Data Reconciliation159
  • 9.1. Introduction159
  • 9.2. The Parameter Estimation Problem160
  • 9.3. Joint Parameter Estimation–Data Reconciliation Problem166
  • 9.4. Dynamic Joint State–Parameter Estimation: A Filtering Approach173
  • 9.5. Dynamic Joint State–Parameter Estimation: A Nonlinear Programming Approach178
  • 9.6. Conclusions179
  • Notation179
  • References181
  • Chapter 10. Estimation of Measurement Error Variances from Process Data183
  • 10.1. Introduction183
  • 10.2. Direct Method184
  • 10.3. Indirect Method185
  • 10.4. Robust Covariance Estimator189
  • 10.5. Conclusions195
  • Notation195
  • References196
  • Appendix A197
  • Chapter 11. New Trends199
  • 11.1. Introduction199
  • 11.2. The Bayesian Approach200
  • 11.3. Robust Estimation Approaches205
  • 11.4. Principal Component Analysis in Data Reconciliation219
  • 11.5 Conclusions223
  • Notation223
  • References224
  • Chapter 12. Case Studies227
  • 12.1. Introduction227
  • 12.2. Decomposition/Reconciliation in a Section of an Olefin Plant228
  • 12.3. Data Reconciliation of a Pyrolysis Reactor233
  • 12.4. Data Reconciliation of an Experimental Distillation Column241
  • 12.5. Conclusions249
  • Notation250
  • References251
  • APPENDIX. STATISTICAL CONCEPTS253
  • 1. Frequency Distributions253
  • 2 Measures of Central Tendency and Spread255
  • 3. Estimation259
  • 4. Confidence Intervals261
  • 5. Testing of Statistical Hypotheses262
  • References263
  • Index265
Book details
  • Vendor Elsevier S & T
  • SKU 9780125944601
  • ISBN-13 9780080530277
  • Author Romagnoli, José A.; Sanchez, Mabel Cristina
  • Category Technology & Engineering
  • Subject Chemical & Biochemical

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Computer techniques have made online measurements available at every sampling period in a chemical process. However, measurement errors are introduced that require suitable techniques for data reconciliation and improvements in accuracy. Reconciliation of process data and reliable monitoring are essential to decisions about possible system modifications (optimization and control procedures), analysis of equipment performance, design of the monitoring system itself, and general management planning. While the reconciliation of the process data has been studied for more than 20 years, there is no single source providing a unified approach to the area with instructions on implementation. Data Processing and Reconciliation for Chemical Process Operations is that source. Competitiveness on the world market as well as increasingly stringent environmental and product safety regulations have increased the need for the chemical industry to introduce such fast and low cost improvements in process operations.

Key Features
* Introduces the first unified approach to this important field
* Bridges theory and practice through numerous worked examples and industrial case studies
* Provides a highly readable account of all aspects of data classification and reconciliation
* Presents the reader with material, problems, and directions for further study