Iterative Learning Control (ILC)-Based Economic Optimization for Batch Processes Using Helpful Disturbance Information

The control strategies for batch processes in the past are usually categorized into two levels. The higher level is economic optimization running at a low frequency and the lower one tracks the reference given at the higher level using MPC or PID. The lower level regards all of the disturbances as something to reject using a quadratics-based optimization objective. However, not all of the disturbances are unfavorable to batch processes; some of them would be helpful. In this paper, an economic optimization for batch processes is directly applied at the lower level. It replaces the conventional tracking strategy. With the collection of the information on disturbances in the previous batches, the iterative learning control strategy (ILC) can determine better operation profiles. ILC has the advantage of continuously improving the economic performance of the current batch with enriched information on disturbances from batch to batch. The convergence of the proposed ILC-based economic optimization is proved. To demonstrate the potential applications of the proposed design method, a typical dynamic batch reactor is applied.