On-line fault detection and supervision in the chemical process industries : selected papers from the IFAC symposium, Newark, Delaware, USA, 22-24 April 1992 /
edited by P.S. Dhurjati and G. Stephanopoulos.
1st ed.
Oxford ; New York : Published for the International Federation of Automatic Control by Pergamon Press, 1993.
xii, 316 p. : ill. ; 30 cm.
0080418961 :
More Details
Oxford ; New York : Published for the International Federation of Automatic Control by Pergamon Press, 1993.
0080418961 :
general note
Papers from the IFAC Symposium on On-line Fault Detection and Supervision in the Chemical Process Industries.
catalogue key
Includes bibliographical references and indexes.
A Look Inside
This item was reviewed in:
SciTech Book News, August 1993
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Main Description
The papers presented at this Symposium were grouped into five major sections: Strategies for the detection and diagnosis of process faults; Modeling, validation, and interpretation of process trends; Supervision and control of chemical plants; Neural networks in process supervision and fault diagnosis. Industrial applications in process supervision and fault diagnosis; The fifty-two papers in this volume cover both theoretical and practical/implementational aspects of systems in the process control industry.
Table of Contents
Section headings and selected papers: Strategies for the Detection and Diagnosis of Process Faults
Robust model-based fault detection in dynamic systems
Structured residuals for fault isolation, disturbance decoupling and modelling error robustness
Facilitated operations using complete and rigorous models
Modeling, Validation and Interpretation of Process Trends
Model-based measurement validation using MFM
A recursive detection scheme for serially correlated process data
The implications of digital communications on sensor validation
Supervision and Control of Chemical Plants
On-line hydrogen resource management in a refinery using fuzzy optimization
Novel method for the optimal control of batch processes
Protection and perpetual supervision systems of oxygen turbocompressors
Neural Networks in Process Supervision and Fault Diagnosis
Use of artificial neural networks to monitor faults and for troubleshooting in the process industries
on the classification characteristics of networks with ellipsoidal activation functions: a comparative study
A decomposition approach to solving large-scale fault diagnosis problems with modular neural networks
Industrial Applications in Process Supervision and Fault Diagnosis
Beyond Falcon: industrial applications of knowledge-based systems
On-line process monitoring, control and supervision for an industrial polymerization process
on line diagnosis of water chemistry in thermal power plant
Author index
Keyword index
Table of Contents provided by Publisher. All Rights Reserved.

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