Carbon dioxide emissions (CO₂) during the dehydration process of natural gas are of important concerns as this gas negatively affects the climate and environment in general. Dehydration process also encounters many heating, cooling and pumping units, which leads to high energy consumption. Reducing these emissions along with minimizing the utilized energy while keeping the high production is a complex problem that can be solved by multi objective optimization (MOO). This study focuses on minimizing CO₂ emissions energy consumption (ENG) along with water content in the gas (WT). This means that the performance of the plant is improved from operational, environmental and energy point of view. The process is simulated in ProMax 4.0 and approved to be valid with the real plant data. Non-dominated sorting genetic algorithm (NSGA-II) was used for attaining the Pareto fronts for the decided MOO cases. The affecting decision variables and limitations are decided based on the capacity of the plant and industrial practice. Two bi-objective cases and a tri-objective case are considered, which are; minimizing CO₂ emissions and WT (Case 1), minimizing ENG and WT (Case 2) and minimizing WT, ENG and CO₂ emissions simultaneously. An attempt to retrofit the current process is also proposed and the cases are carried out with the modified process. Results showed noticeable improvements and enhancements.

天然气脱水过程中的二氧化碳排放量（CO ₂）具有重要意义，因为这种气体通常会对气候和环境产生负面影响。脱水过程还遇到许多加热，冷却和抽水装置，这导致高能耗。在保持高产量的同时减少这些排放并最大程度地利用能源是一个复杂的问题，可以通过多目标优化（MOO）解决。这项研究的重点是减少CO ₂排放能量消耗（ENG）以及气体中的水分含量（WT）。这意味着从运行，环境和能源的角度来看，该工厂的性能得到了改善。该过程在ProMax 4.0中进行了仿真，并被批准对实际工厂数据有效。非决定性排序遗传算法（NSGA-II）用于确定MOO案例的Pareto前沿。影响决策变量和局限性取决于工厂的能力和工业实践。考虑了两个双目标情况和一个三目标情况。最小化CO ₂排放和WT（案例1），最小化ENG和WT（案例2），最小化WT，ENG和CO ₂同时排放。还提出了对当前流程进行改造的尝试，并在修改后的流程下进行案例处理。结果显示出明显的改进和增强。