P. Tessier

Paper: Dual adaptive control of chip refiner motor load

A characteristic of wood chip refiners is that the static gain between the input (plate gap) and the output (motor load) is both nonlinear and time-varying, with reversal in the sign of the gain indicating the onset of pulp pad collapse towards lower values of the plate gap. The control objective is to regulate the motor load while avoiding pad collapse. The problem is principally stochastic in nature, since the gap at which gain reversal occurs can wander unpredictably. The control strategies developed in this paper are based primarily on viewing the refiner as a plant with uncertain linear characteristics. The proposed strategy consists of a multimodel parameter estimator (AFMM), a new active suboptimal dual controller, and some simple heuristic logic to deal with the nonlinearities. Several different combinations were evaluated by conducting a series of trials on an industrial refiner. The results show that while a more conventional estimator might have been used, the extra effort required to implement the dual controller appears to have been worthwhile, since probing aided significantly in the identification of gain changes and helped to prevent turn-off. The results also show that a variable retract strategy worked better than using a constant retract term for preventing operation in the pad collapse region, and that the control performance could be improved by identifying the maximum load the first time a pad collapse is encountered, and then using this information to set an upper limit on the set point.

Process modelling and optimization of systems with imprecise and conflicting equations

Abstract 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.

Optimization of a wood chip refining process based on fuzzy relational models

The authors developed a fuzzy relational model for an industrial wood chip refining process. In conjunction with an optimization technique, this model is used either to minimize energy consumption of the refiner operation while maintaining pulp properties within acceptable limits, or to optimize the pulp properties within the refiner operation range. The model has been implemented in a western Canadian pulp and paper mill.