Staged combustor control using artificial neural network-based process models

Document Type

Article

Publication Date

3-1-2006

Abstract

Process controllers using trained, feed-forward, multi-layer-perceptron (FMLP) neural networks as complex process models have been successfully demonstrated for the active, on-line control of selected species emitted from a two-stage combustion reactor. In the first case, as compared to a proportional-integral-derivative controller, faster control of exhaust oxygen content with nearly no offset was achieved using a proportional controller with a variable bias value as determined by an FMLP. In the second case, effective and rapid control of exhaust nitrogen oxide, after a separate feed stream disturbance and a set point change, was achieved using a controller comprised of two clusters of FMLP neural networks. The first cluster identified the process disturbance and adjusted the manipulated variable. The second cluster served as a Smith time-delay compensator. All the FMLP networks used were trained off-line using steady-state data obtained from both experiments and from direct combustor simulations based on detailed chemical reactions. Copyight © Taylor & Francis Inc.

Identifier

30544434236 (Scopus)

Publication Title

Chemical Engineering Communications

External Full Text Location

https://doi.org/10.1080/009864491007796

e-ISSN

15635201

ISSN

00986445

First Page

386

Last Page

401

Issue

3

Volume

193

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