WIT Press

Neural Network Self-Tuning Control Of Hot- Spot Temperature In A Fixed-Bed Catalytic Reactor

Price

Free (open access)

Paper DOI

10.2495/AI970361

Volume

19

Pages

12

Published

1997

Size

152 kb

Author(s)

N. Mazana & M. Nzama

Abstract

Neural Network Self-Tuning Control Of Hot- Spot Temperature In A Fixed-Bed Catalytic Reactor N. Mazana’, M. Nzama@ # Department of Chemical Engineering, ‘Department of Electronic Engineering, National Universig of Science and Technology P.O. Box 346, Bulawayo, Zimbabwe. Email: nustelec@esanet.zw ABSTRACT This paper demonstrates the possibility of applying artificial neural networks (ANNs) in the self-tuning PID control of the hot-spot-temperature in a fixed-bed catalytic reactor system. In this reactor system sulfur dioxide is oxidized using vanadium pentoxide catalyst. Unlike the conventional self-tuning PID control algorithm, the ANN applied to the self-tuning PID (NNW-PID) philosophy is an inherent nonlinear estimator and therefore identifies a nonlinear system directly from historical data supplied by the plant. In the majority of control applications, the ANN is employed as a predictor of future outputs within an established predictive control algorithm [1,2]. In this paper we propose a

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