Design and Optimization of Methanol Production using PyBOUND

Abstract

In this paper, we study the design optimization of methanol production with the goal of minimizing methanol production cost. One challenge of methanol production via carbon dioxide (CO2) hydrogenation is the reduction of operating costs. The simulation of methanol production is implemented within the Aspen HYSYS simulator. The feeds are pure hydrogen and captured CO2. The process simulation involves a single reactor and incorporates recycling at a ratio of 0.995. The methanol production cost is determined using an economic analysis. The cost includes capital and operating costs, which are determined through the equations and data from the capital equipment-costing program. The decision variables are the pressure and temperature of the reactor contents. The optimization problem is solved using a derivative-free algorithm, pyBOUND, a Python-based black-box model optimization algorithm that uses random forests (RFs) and multivariate adaptive regression splines (MARS). The predicted minimum methanol production cost by pyBOUND is $1396.56 per tonne of methanol, which corresponds to the pressure of 68.82 bar and temperature of 192.23�C while the actual cost is $1393.95 per tonne of methanol at these conditions. The cost breakdown of methanol production is 75% hydrogen price, 11% utility cost, 8% capital cost, 5% carbon dioxide price, and 1% operating cost.