Integrating Multiobjective Optimization with Technoeconomic Analysis: Advancing Carbon Capture via a Deep Eutectic Solvent of Choline Chloride−Glycerol

Abstract Deep eutectic solvent is cost-effective, easily available, and ionic liquid-like, making them economical alternatives for CO2 capture. This study develops a CO2 capture process using choline chloride-glycerol as an absorbent, employing single-parameter optimization to narrow variable interval and multiobjective optimization for focusing on CO2 purity, capture rate, and power consumption. The single-parameter optimization scheme first achieves a CO2 purity and capture rate of 99.70% and 94.08%, respectively, with a power consumption of 2.13 GJ/t CO2. Subsequently, the multiobjective optimization scheme combining the genetic algorithm and the artificial neural network reduces power consumption to 2.02 GJ/t CO2, with higher CO2 purity and capture rate. Economically, the CO2 capture cost decreases by 1.9 $/t CO2 compared with the single-parameter optimized scheme. Despite higher solvent consumption, the low price of deep eutectic solvent keeps the absorbent cost at a reasonable level and achieves good technoeconomic performance. The integrated optimization method combining single-parameter and multiobjective strategies effectively balances multiple conflicting performance indicators of the process. These findings offer critical insights for designing and optimizing CO2 capture systems in coal-fired power plants as well as other emission-intensive industries.