Batch-to-batch optimization using neural network models

As chemical plants become more flexible, the importance of batch processing has increased in recent years. Batch processes are also used in emerging areas such as semiconductor manufacturing. In order to derive the maximum benefit from batch processes, it is important that their operation be optimized. However, such optimization can be difficult since batch processes often involve complex, nonlinear phenomena. In this paper an approach to batch-to-batch optimization is coupled with neural network modeling to improve the performance of batch processes. The neural models yield results that are comparable to those achieved with first principle models. This accuracy is achieved through the use of feedback from each batch which effectively compensates for plant-model mismatch.