By
Dr. Shankar Narasimhan, Ph.D. (Ch.E.), Shankar Narasimhan, Ph.D. (Ch.E.), has participated in the practical design and implementation of an industrial package for data reconciliation
and gross error detection. He has published many significant papers on various aspects of this vast area since receiving his MS and Ph.D.
in Chemical Engineering from Northwestern University.
Dr. Cornelius Jordache, Ph.D. (Ch.E), Cornelius Jordache, Ph.D. (Ch.E), received his MS and Ph.D. in Chemical Engineering from Northwestern University. He has since held leading
industrial positions developing online data reconciliation and gross error detection systems. He also has extensive experience in training
engineers and other personnel in the implementation and maintenance of data reconciliation software.
Description
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.
