Multi-objective operation optimization of ethylene cracking furnace based on AMOPSO algorithm

Abstract The objective of this article is to research and design a multi-objective operation optimization strategy and comprehensive evaluation method of solutions, to efficiently solve the multi-objective operation optimization problem of ethylene cracking furnace. An adaptive multi-objective particle swarm optimization (AMOPSO) algorithm is proposed and developed based on dynamic analytic hierarchy process (AHP). The algorithm adopts fuzzy consistent matrix to select the global best solution, which ensures the right direction of particle evolution. Furthermore, the evolution state is measured to adjust the weight and learning coefficients adaptively. The proposed method is applied to the operation optimization of ethylene cracking furnace. Two cases are studied including the fixed cracking cycle with four objectives and the non-fixed cracking cycle with five objectives. According to the preferences, decision makers can select the appropriate operation optimization conditions from alternative Pareto optimal solutions by the results of fuzzy evaluation. A feasible solution is provided for the multi-objective operation optimization of ethylene cracking furnace.