Batch-to-batch model improvement for cooling crystallization

Two batch-to-batch model update strategies for model-based control of batch cooling crystallization are presented. In Iterative Learning Control, a nominal process model is adjusted by a non-parametric, additive correction term which depends on the difference between the measured output and the model prediction in the previous batch. In Iterative Identification Control, the uncertain model parameters are iteratively estimated using the measured batch data. Due to the different nature of the model update, the two algorithms have complementary advantages and disadvantages which are investigated in a simulation study and through experiments performed on a pilot-scale crystallizer. (C) 2015 Elsevier Ltd. All rights reserved.

Publication year:2015

Keywords:System identification, Iterative learning control, Process control, Batch processes, Batch cooling crystallization

CSS-citation score:1