Measurement-based Optimization in the Batch Chemical Industry

The optimal operation of batch processes has attracted more attention in recent years since, in the face of growing competition, it is a natural choice for reducing production costs, improving product quality, and meeting safety requirements and environmental regulations. Since the models currently available in industry are poor and carry a large amount of uncertainty, standard model-based optimization techniques are by and large ineffective, and optimization methods need to rely more on measurements. This paper presents a new framework, whereby important characteristics of the optimal solution are identified and serve as references to a feedback control scheme. Thus, optimality is achieved by tracking, with no numerical optimization being required on-line. When only batch-end measurements are available, the proposed method leads naturally to an efficient batch-to-batch optimization scheme. The approach is illustrated via simulation of a semi-batch reactor in the presence of uncertainty.