GREY BOX MODELLING – BRANCHES AND EXPERIENCES

Abstract

This paper deals with methods and experiences of incorporating a priori knowledge into mathematical models of industrial processes and systems. Grey box modelling has been developed in several directions and can be grouped into branches depending on the way a priori knowledge is handled. In this paper we divide grey box modelling into the following branches; constrained black box identification, semi-physical modelling, mechanistic modelling, hybrid modelling and distributed parameter modelling. Experiences from case studies demonstrate the different branches of grey box modelling procedures. In the applications, the grey box models have been used for model based control, soft sensors, process supervision and failure detection. Further, distributed parameter modelling presents a specific challenge in that it is difficult to distinguish model reduction errors from model-data discrepancies. By estimating the model reduction error and forming hypothesis tests based on the estimate, the problem can be dealt with effectively.