Multiobjective optimization for safety-related decision making in chemical processes

This paper proposes a novel decision making procedure based on multiobjective optimization programming (MOOP) to find the investment priority to reduce plant accidents. The new method rank the accident scenarios in order among many possible accident scenarios in a plant to decide the invest priority as considering safety and cost simultaneously. The method is explained by taking an example, which includes 30 accident scenario data (Kim et al., 2001) consisting of accident consequence, accident frequency, safety activity cost, and non-operating time. In this example, four goals are considered: (1) minimization of total safety activity cost, (2) minimization of total accident consequence, (3) minimization of the number of accident scenarios for unreasonable frequency, and (4) minimization or non-operating time. To analyze this optimization problem in terms or process safety and cost, this study obtained the noninferior solution curve (Pareto curve) by using the Goal programming (GP) methods. We found the ideal compromise solution set based on the Pareto curve. This result assists the business decision maker to select the best compromise in improving process safety as well as reducing investment cost.