Multi-objective optimization of industrial gas-sweetening operations using economic and environmental criteria

Multi-objective optimization of industrial natural gas sweetening process using elitist non-dominated sorting genetic algorithm is carried out for methyl di-ethanol amine absorbent to tune the process parameters to improve absorption as well as regeneration performance. This study includes several operating parameters such as lean amine temperature and pressure, feed gas temperature and pressure, regenerator feed temperature and pressure, feed flow rate, etc. Environmental and economic optimization criteria are considered using four cases comprising of several conflicting objectives like net profit, global warming potential, and acidification potential under two-objective and three-objective optimization scenarios. Constraints are imposed on H₂S content and CO₂ content as per the maximum permissible limit. Pareto optimal fronts are found for different cases and trade-offs between different objectives are illustrated. Simultaneous effects of different variables together on the conflicting objectives are considered to analyze the sweetening process to get a more economical and environmentally friendly process.