Abstract
This paper addresses methods of knowledge engineering for chemical and biochemical process modelling and control. The concept of Knowledge Based Modular (KBM) Networks is presented. KBM networks represent a method of expressing and combining different types of knowledge, usually available for modelling chemical and biochemical processes: mechanistic, heuristic and knowledge hidden in process data records. The Expectation Maximisation (EM) algorithm is employed to optimally combine the modules within the KBM network. The concepts are illustrated with an application to a baker's yeast production process. The results show that it is possible to obtain more accurate process description when all available sources of knowledge are incorporated in the process model.
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