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.
CITATION STYLE
Xiong, Z., Zhang, J., Wang, X., & Xu, Y. (2005). Neural network based on-line shrinking horizon re-optimization of fed-batch processes. In Lecture Notes in Computer Science (Vol. 3498, pp. 839–844). Springer Verlag. https://doi.org/10.1007/11427469_133
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