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Run-to-run iterative optimization control of batch

Lookup NU author(s): Dr Jie ZhangORCiD


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Recurrent neural network (RNN) is used to model product quality of batch Processes from Process operational data. Due to model-plant mismatches and unmeasured disturbances, the calculated control policy based on the RNN model may not be optimal when applied to the actual Process. Model prediction errors from previous runs are used to improve RNN model predictions for the current run. It is proved that the modified model errors are reduced from run to run. Consequently control trajectory gradually approaches the optimal control policy. The proposed scheme is illustrated on a simulated batch reactor © Springer-Verlag 2004.

Publication metadata

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

Publication type: Book Chapter

Publication status: Published

Book Title: Advances in Neural Networks - ISNN 2004

Year: 2004

Volume: 3174

Pages: 97-103

Print publication date: 01/01/2004

Series Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Publisher: Springer

Place Published: Berlin


DOI: 10.1007/978-3-540-28648-6_15

Library holdings: Search Newcastle University Library for this item

ISBN: 9783540228431