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

Xiong, Z; Zhang, J; Wang, X; Xu, Y

doi:10.1007/11427469_133

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

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.

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. © Springer-Verlag Berlin Heidelberg 2005.