Data processing and reconciliation for chemical process operations [electronic resource]

• 作者： Romagnoli, Jose A. (Jose Alberto)
• 其他作者：
  • Sanchez, Mabel Cristina.
  • ScienceDirect (Online service)
• 出版： San Diego, Calif. ;London : Academic
• 叢書名： Process systems engineering ;v. 2
• 主題： Chemical process control--Data processing , Chemical processes , Chemical process control , Electronic books.
• ISBN： 9780125944601 (paper) 、 0125944608 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: General Introduction. Reliable and Complete Knowledge. Some Issues Associated with a General Data Reconciliation Problem. About This Book. References of Chapter 1. Estimability and Redundancy Within the Framework of the General Estimation Theory. Introduction. Basic Concepts and Definitions. Decomposition of the General Estimation Problem. Structural Analysis. Conclusions. Notation. References of Chapter 2. Appendix 2 - A. Classification of the Process Variables for Chemical Plants. Introduction. Modeling Aspects. Classification of Process Variables. Analysis of the Process Topology. Different Approaches for Solving the Classification Problem. Use of Output Set Assignments for Variable Classification. The Solution of Special Problems. A Complete Classification Example. Formulation of a Reduced Reconciliation Problem. Conclusions. Notation. References of Chapter 3. Appendix 3 - A. Appendix 3 - B. Decomposition Using Orthogonal Transformations. Introduction. Linear Mass Balances. Bilinear Multicomponent and Energy Balances. Conclusions. Notation. References of Chapter 4. Steady State Data Reconciliation. Introduction. Problem Formulation. Linear Data Reconciliation. Non-Linear Data Reconciliation. Conclusions. Notation. References of Chapter 5. Appendix 5 - A. Sequential Processing of the Information. Introduction. Sequential Processing of the Constraints. Sequential Processing of the Measurements. Alternative Formulation from Estimation Theory. Conclusions. Notation. References of Chapter 6. Appendix 6 - A. Treatment of Gross Errors. Introduction. Gross Error detection. Identification of the Measurements with Gross Error. Estimation of the Magnitude of Bias and Leaks. A Recursive Scheme for Gross Error Identification and Estimation. Conclusions. Notation. References of Chapter 7. Appendix 7 - A. Appendix 7 - B. Rectification of Process Measurement Data in Dynamic Situations. Introduction. Dynamic Data Reconciliation: A Filtering Approach. Dynamic Data Reconciliati
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• 摘要註: 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.
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