Iterative Learning Control of a Batch Cooling Crystallization Process based on Linear Time- Varying Perturbation Models

Abstract The paper presents an approach to improve the product quality from batch to batch by exploiting the repetitive nature of batch processes to update the operating trajectories using process knowledge obtained from previous runs. The data-based optimization methodology is based on using the linear time varying (LTV) perturbation model in an iterative learning control (ILC) framework to provide a convergent batch-to-batch improvement of the process performance indicator. The approach was evaluated for a batch cooling crystallization process with the aim to control the mean crystal size by manipulating the reactor temperature profile. The simulated temperature trajectories resulting from the iterative measurement-based optimization approach converged to the theoretically optimal trajectory obtained using model-based optimization. These results demonstrate the potential of the ILC approach for controlling batch processes without rigorous process models.