A batch-to-batch iterative optimal control strategy based on recurrent neural network models

A batch-to-batch model-based iterative optimal control strategy for batch processes is proposed. To address the difficulties in developing detailed mechanistic models, recurrent neural networks are used to model batch processes from process operational data. Due to model-plant mismatches and unmeasured disturbances, the calculated optimal control profile may not be optimal when applied to the actual process. To address this issue, model prediction errors from previous batch runs are used to improve neural network model predictions for the current batch. Since the main interest in batch process operation is on the end of batch product quality, a quadratic objective function is introduced to track the desired qualities at the end-point of a batch. Because model errors are gradually reduced from batch-to-batch, the control trajectory gradually approaches the optimal control policy. The proposed scheme is illustrated on a simulated methyl methacrylate polymerisation reactor.