Abstract
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.
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Xiong, Z., Zhang, J., Wang, X., Xu, Y. (2004). Run-to-Run Iterative Optimization Control of Batch Processes Based on Recurrent Neural Networks. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_15
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DOI: https://doi.org/10.1007/978-3-540-28648-6_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
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