PERFORMANCE ANALYSIS OF ONLINE BATCH OPTIMIZATION SYSTEMS

In this paper, the on-line optimization of batch reactors under parame tric uncertainty is considered. A method is presented that estimates t he likely economic performance of the on-line optimizer. The method of orthogonal collocation is employed to convert the differential algebr aic optimization problem (DAOP) of the dynamic optimization into a non linear program (NLP) and determine the nominal optimum. Based on the r esulting NLP, the optimization steps are approximated by neighbouring extremal problems and the average deviation from the true process opti mum is determined dependent on the measurement error and the parametri c uncertainty. A back off from the active path and endpoint constraint s is determined at each optimization step which ensures the feasible o peration of the process. The method of the average deviation from opti mum is developed for time optimal problems. The theory is demonstrated on an example.