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Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes (Paperback, 2000 ed.)
Loot Price: R2,765
Discovery Miles 27 650
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Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes (Paperback, 2000 ed.)
Series: Advances in Industrial Control
Expected to ship within 10 - 15 working days
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Early and accurate fault detection and diagnosis for modern
chemical plants can minimise downtime, increase the safety of plant
operations, and reduce manufacturing costs. The process-monitoring
techniques that have been most effective in practice are based on
models constructed almost entirely from process data. The goal of
the book is to present the theoretical background and practical
techniques for data-driven process monitoring. Process-monitoring
techniques presented include: Principal component analysis; Fisher
discriminant analysis; Partial least squares; Canonical variate
analysis.
The text demonstrates the application of all of the data-driven
process monitoring techniques to the Tennessee Eastman plant
simulator - demonstrating the strengths and weaknesses of each
approach in detail. This aids the reader in selecting the right
method for his process application. Plant simulator and homework
problems in which students apply the process-monitoring techniques
to a nontrivial simulated process, and can compare their
performance with that obtained in the case studies in the text are
included. A number of additional homework problems encourage the
reader to implement and obtain a deeper understanding of the
techniques.
The reader will obtain a background in data-driven techniques for
fault detection and diagnosis, including the ability to implement
the techniques and to know how to select the right technique for a
particular application.
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