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Neural network based on-line shrinking horizon re-optimization of fed-batch processes

Lookup NU author(s): Dr Jie ZhangORCiD


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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. © Springer-Verlag Berlin Heidelberg 2005.

Publication metadata

Author(s): Xiong Z, Zhang J, Wang X, Xu Y

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Advances in Neural Networks – ISNN 2005. Second International Symposium on Neural Networks

Year of Conference: 2005

Pages: 839-844

ISSN: 0302-9743

Publisher: Springer


DOI: 10.1007/11427469_133

Notes: book doi: 10.1007/b136479

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783540259145