Multi-objective optimization of a methanol synthesis process: CO₂ emission vs. economics

Abstract

This work addresses the modeling and multi-objective optimization of methanol synthesis to efficiently utilize CO₂ from the CO₂ emissions and economics perspectives. Kinetic reactors for reforming and methanol synthesis reactions were used in the process simulator for modeling the entire process, and multi-objective optimization was conducted using the developed process model to maximize CO₂ reduction and the economic profit. The feed composition, operating temperature and pressure of the reformer, and utility temperature of the methanol synthesis reactor were considered as arguments in the non-dominated sorting genetic algorithm (NSGA II) method with the net change of CO₂ and economic profit as the objective elements, and the Pareto front showed a trade-off between CO₂ reduction and economic profit. When the amount of CH₄ in the feed was fixed at 500 kmol/h, CO₂ reduction was 11,588 kg/h, whereas the profit was −5.79 million dollars per year. Meanwhile, a maximum profit of 20 million dollars per year resulted in CO₂ emissions of 7,201 kg/h. The feed composition had the most significant influence on both objective elements (net change of CO₂ and economics); as CO₂ in the feed increased, CO₂ reduction increased and profit decreased, while the increase of H₂O in the feed increased CO₂ emissions and profit.