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Modeling of a fixed-bed reactor using the K-L expansion and neural networks

✍ Scribed by Xing-Gui Zhou; Liang-Hong Liu; Yin-Chun Dai; Wei-Kang Yuan; J.L. Hudson

Publisher: Elsevier Science
Year: 1996
Tongue: English
Weight: 597 KB
Volume: 51
Category: Article
ISSN: 0009-2509
DOI: 10.1016/0009-2509(96)00075-9

No coin nor oath required. For personal study only.

✦ Synopsis

Karhunen-Loeve expansion and feedtbrward neural networks are combined together in modeling a wall cooled fixed-bed reactor lbr its on-line perlbrmance prediction. The K-L expansion is employed as a preprocessor of neural networks while the latter is used to generate the coefficients of the K-L expansion. The perlormance oflhe KL-NN model is investigated b3' both experimentation and simulation with benzene oxidation as a working system. It is shown that the method is effective tbr on-line prediction of the bed temperatures. Our conclusions are more important than just that one term can be used. Sometimes it might be two or three, but the method desctribed in the paper is still powerful.

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