A GENERAL FRAMEWORK FOR PREVENTIVE MAINTENANCE OPTIMIZATION IN CHEMICAL PROCESS OPERATIONS

Chemical process reliability has become more recognized both in terms of its impact on economics, and for providing academically challenging problems. In this work, we give an overview of some of the major chal lenges in formulating and optimizing preventive maintenance. As a resu lt, we propose a general framework for preventive maintenance optimiza tion that combines Monte Carlo simulation with a genetic algorithm. Th is proposed approach has distinct advantages. When applied to opportun istic maintenance problems, the method developed overcomes demonstrate d shortcomings with analytic or Markov techniques in terms of solution accuracy, versatility, and tractability. The framework is easily inte grable with general process planning and scheduling, and it provides s ensitivity analysis. Furthermore, a genetic algorithm combines well wi th Monte Carlo simulation to optimize a non-deterministic objective fu nction. (C) 1997 Elsevier Science Ltd.