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.

这项工作解决了甲醇合成的建模和多目标优化问题，以从 CO ₂排放和经济角度有效利用 CO _{2 。}在过程模拟器中使用用于重整和甲醇合成反应的动力学反应器对整个过程进行建模，并使用开发的过程模型进行多目标优化，以最大限度地减少CO ₂和经济效益。_{以CO 2}净变化的非支配排序遗传算法（NSGA II）方法中的进料组成、重整器的操作温度和压力以及甲醇合成反应器的使用温度为参数_{和经济利润作为客观要素，帕累托前沿表现出CO 2}减排量和经济利润之间的权衡。当饲料中的 CH ₄量固定为 500 kmol/h 时，CO ₂减少量为 11,588 kg/h，而每年的利润为 -579 万美元。同时，每年 2000 万美元的最大利润导致 CO ₂排放量为 7,201 kg/h。进料组成对两个目标要素（CO ₂的净变化和经济性）影响最大；随着进料中CO ₂的增加，CO ₂减少量增加，利润减少，而进料中H ₂ O 的增加使CO _2增加排放和利润。