How to increase the performance of models for process optimization and control

Abstract

Some key aspects of obtaining hybrid process models which perform well and that can be used in process supervision, optimization and control are discussed from the point of view of the benefit/cost-ratio. The importance of starting with a clear definition of the problem and a corresponding quantitative objective function is shown. In order to enhance the benefit/cost-ratio above the threshold of acceptance, a series of procedures is proposed: in the beginning an exploratory process data analysis is suggested to classify the process variables according to their importance and to facilitate the development of black- and grey-box models. Efficient validation of the model is shown to be indispensable. Hybrid model approaches proved to have to significant advantages, since they allow the activation of a larger portion of the available a-priori knowledge. Applications of hybrid models with respect to process optimization require new techniques, since the classical approaches are too difficult to use and are restricted to well-performing models. Finally, powerful software tools are required to implement the different algorithms at the production plants and to allow the efficient conversion of the ideas to real benefits.