By Dr. Shankar Narasimhan Ph.D. (Ch.E.)

ISBN-10: 0884152553

ISBN-13: 9780884152552

  • This is a superb publication at the topic - the authors have coated all of the bases. if you'd like a publication on info reconciliation and gross blunders detection, this can be as entire and thorough a booklet as i will be able to think. - Les A. Kane, Editor, complicated approach regulate and knowledge structures

Content:
Acknowledgments

, Pages xiii-xiv
Preface

, Pages xv-xvii
1 - the significance of knowledge Reconciliation and Gross errors Detection

, Pages 1-31
2 - dimension blunders and blunder aid Techniques

, Pages 32-58
3 - Linear Steady-State information Reconciliation

, Pages 59-84
4 - Steady-State facts Reconciliation for Bilinear Systems

, Pages 85-118
5 - Nonlinear Steady-State info Reconciliation

, Pages 119-141
6 - facts Reconciliation in Dynamic Systems

, Pages 142-173
7 - creation to Gross mistakes Detection

, Pages 174-225
8 - a number of Gross errors id options for Steady-State Processes

, Pages 226-280
9 - Gross errors Detection in Linear Dynamic Systems

, Pages 281-299
10 - layout of Sensor Networks

, Pages 300-326
11 - commercial purposes of knowledge Reconciliation and Gross mistakes Detection Technologies

, Pages 327-372
Appendix A - simple innovations in Linear Algebra

, Pages 373-377
Appendix B - Graph conception Fundamentals

, Pages 378-383
Appendix C - basics of likelihood and Statistics

, Pages 384-393
Index

, Pages 394-402
Author Index

, Pages 403-405
The Authors

, Page 406

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Additional resources for Data Reconciliation and Gross Error Detection. An Intelligent Use of Process Data

Example text

Instrument types of faults. Reproducedwith permission of the American Institute of Chemical Engineers. Copyright © 1996 AIChE. All rights reserved. them, such as occasional outliers (spikes), can be detected by using special filtering techniques or statistical quality control (also known as statistical process control). Other types might be more difficult to detect without a physical model. Data reconciliation is the appropriate tool in most cases. ERROR REDUCTION METHODS Analog and digital filters have been widely used to reduce random errors (high-frequency noise) in process values.

Barnes). New York: AIME, 1982. 19. Stanley, G. H. Mah. " AIChE Journal 23 (1977): 642-650. 20. Almasy, G. A. " AIChE Journal 36 (1991): 1321-1330. 21. Liebman, M. , T. F. Edgar, and L. S. Lasdon. " Computers Chem. Engng. 16 (1992): 963-986. 22. Ripps, D. L. " Chem. Eng. Progr. Symp. Ser. No. 55 61 (1965): 8-13. 23. Reilly, P. , and R. E. Carpani. "Application of Statistical Theory to Adjustment of Material Balances," presented at the 13th Can. Chem. Eng. , Montreal, Quebec, 1963. 24. Almasy, G.

Application of Statistical Theory to Adjustment of Material Balances," presented at the 13th Can. Chem. Eng. , Montreal, Quebec, 1963. 24. Almasy, G. A, and T. Sztano. " Prob. Control Inform. Theory 4 (1975): 57-69. 25. H. Mah. " AIChE Journal 33 (1987): 1514-1521. 26. Tamhane. A. , C. H. Mah. "A Bayesian Approach to Gross Error Detection in Chemical Process Data. " Chemometrics and Intel. Lab. Sys. 4 (1988): 33. 27. , and C. M. Crowe. " AIChE Journal 41 (1995): 1712-1722. 30 Data Reconciliation and Gross Error Detection 28.

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Data Reconciliation and Gross Error Detection. An Intelligent Use of Process Data by Dr. Shankar Narasimhan Ph.D. (Ch.E.)


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