Multi-objective optimization of methanol reforming reactor performance based on response surface methodology and multi-objective particle swarm optimization coupling algorithm for on-line hydrogen production

Abstract

The utilization of high-temperature exhaust to drive methanol steam reforming for on-line hydrogen production in engines is an effective approach to solving hydrogen storage and transportation challenges. In this work, the response surface methodology (RSM) is combined with the multi-objective particle swarm optimization (MOPSO) algorithm, and is first utilized for multi-objective optimization of the performance of a methanol reforming reactor. Regression models for optimization objectives are developed based on RSM, and the reliability and significance of regression models are determined through analysis of variance. The influence mechanism of the interaction between design variables on optimization objectives is elucidated by visual analysis of the response surface. A multi-objective optimization of reactor performance is performed utilizing the MOPSO algorithm, and the Pareto optimal solution is obtained from the Pareto optimal frontier based on the technique for order preference by similarity to ideal solution method. Optimization results demonstrate that the Pareto optimal solution is obtained at an exhaust temperature of 585.21 K, an exhaust flow rate of 0.0097 kg·s−1, a steam to methanol ratio of 1.48, and a weight hourly space velocity of 1.2. The hydrogen yield, methanol conversion and carbon monoxide selectivity corresponding to the Pareto optimal solution are 68.53 mol·h−1, 0.83 and 0.011, respectively. Under optimal operating parameters, the methanol reforming reactor achieves 21.92 % exhaust energy recovery.