Abstract
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.
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Xiong, Z., Zhang, J., Wang, X., Xu, Y. (2005). Neural Network Based On-Line Shrinking Horizon Re-optimization of Fed-Batch Processes. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_133
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DOI: https://doi.org/10.1007/11427469_133
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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