Dynamic case-based reasoning for process operation support systems

Abstract Modern process industry is faced with ever-increasing requirements for better quality, higher production profits, safer operation, and stringent environment regulation. New technologies are required to reduce the operator's cognitive load and achieve more consistent operations. Operation support systems, which help operators in obtaining effective and timely decisions, have attracted much attention. The research described here intended to develop an efficient reasoning method for operation support systems. It is pointed out that case-based reasoning (CBR), which is based on the concept that human memory is episodic in nature, is consistent with operator's problem solving. Despite their successful application to the solution of many problems, case-based reasoning methods are mostly static. Process operation support systems require a CBR method that can represent system dynamics and fault-propagation phenomena. To solve this problem, a new approach, namely dynamic case-based reasoning (DCBR), is developed. DCBR introduces a number of new mechanisms including time-tagged indexes, dynamic and composite features, and multiple indexing paths. As a result, it provides flexible case adaptation, timely and accurate problem solving, and an ability to incorporate other computational and reasoning methods.

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