The University of British Columbia

For industrial processes, mechanistic models are not always available due to incomplete knowledge and imprecise descriptions of the phenomena that take place in the process. However, through years of practical operation, empirical knowledge of these processes can be accumulated and represented by a set of imprecise and empirical equations. Unfortunately, these equations may sometimes be redundant and even contradictory. A fuzzy-logic-based modelling and optimization technique is proposed for representing uncertainty and approximation in relationships among process variables. The process model is represented on three levels: heuristic knowledge base, fuzzy equation sets, and fuzzy relation matrix. The model is used to maximize the degree of compatibility and minimize conflicts among the fuzzy equations via a genetic algorithm. This work illustrates a new, important feature of fuzzy modelling: the ability to handle conflict among system equations. The new approach has been applied to fuzzy optimization of pulp quality control of an industrial wood chip refiner.