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

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.