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Data Reconciliation and Gross Error Detection: An Intelligent Use of Process Databy Shankar Narasimhan
Synopses & Reviews
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.
Book News Annotation:
Intended to help process engineers analyze and correct potential problems, this volume provides a systematic and detailed treatment of available methods for applying data reconciliation techniques to many different systems such as linear, bilinear, nonlinear, dynamic, and industrial. Narasimhan (chemical engineering, Indian Institute of Technology, Madras, India) and Jordache (a corporate data reconciliation specialist and former assistant professor with the Polytechnic Institute of Bucharest, Romania) also explain data filtering, data compression, and the impact of measurement selection on data reconciliation.
Annotation c. Book News, Inc., Portland, OR (booknews.com)
Table of Contents
: Introduction. Measurement Errors and Error Reduction Techniques. Steady State Data Reconciliation for Bilinear Systems. Nonlinear Steady State Data Reconciliation. Data Reconciliation in Dynamic Systems. Introduction to Gross Error Detection. Multiple Gross Error Identification Strategies for Steady State Processes. Gross Error Detection in Dynamic Processes. Design of Sensor Networks. Industrial Applications of Data Reconciliation and Gross Error Detection Technologies. Appendix A: Basic concepts of linear algebra. Appendix B: Basic concepts of Graph Theory. Appendix C: Statistical Hypotheses Testing.
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