SEMIBATCH PROCESS OPTIMIZATION UNDER UNCERTAINTY - THEORY AND EXPERIMENTS

Batch and semi-batch processes provide a flexible means of producing h igh value-added products in the chemical, biotechnical, and pharmaceut ical industries. Unlike continuous processes, batch processes are inhe rently transient and typically also nonlinear, and a nonlinear dynamic end-point optimization problem needs to be solved in order to determi ne the optimal operating strategy. Inter-and intra-run variations and lack of better measurement information typically only allow us to buil d an imperfect model of the process, and the remaining uncertainties c an be large. Nominal optimization techniques, requiring an exact proce ss model, may thus not always be suitable for batch process optimizati on. This contribution suggests an alternative approach in which modeli ng and identification uncertainty are explicitly accounted for during the process optimization. As opposed to a deterministic quantity that is a function of only the nominal model, a probabilistic measure of su ccess is optimized, leading to robustness of the desired objective to uncertainties and variations. Both simulation and experimental results are given to demonstrate the idea and the application of the proposed approach and to highlight the benefits that can be expected from its industrial application. (C) 1997 Elsevier Science Ltd.