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Neural network based on-line shrinking horizon re-optimization of fed-batch processes. (English) Zbl 1084.68820

Wang, Jun (ed.) et al., Advances in neural networks – ISNN 2005. Second international symposium on neural networks, Chongqing, China, May 30 – June 1, 2005. Proceedings, Part III. Berlin: Springer (ISBN 3-540-25914-7/pbk). Lecture Notes in Computer Science 3498, 839-844 (2005).
Summary: Neural network is used to model fed-batch processes from process operational data. Due to model-plant mismatches and unknown disturbances, the off-line calculated control policy based on the neural network models may no longer be optimal when applied to the actual process. Thus the control policy should be re-optimized. Based on the mid-batch process measurements, on-line shrinking horizon optimization is carried out for the remaining batch period. The iterative dynamic programming algorithm based on neural network models is developed to solve the on-line optimization problem. The proposed scheme is illustrated on a simulated fed-batch chemical reactor.
For the entire collection see [Zbl 1073.68015].

MSC:

68T05 Learning and adaptive systems in artificial intelligence
92B20 Neural networks for/in biological studies, artificial life and related topics
93C95 Application models in control theory
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