Impacts of hydrogen price and carbon dioxide emission factor on bi-objective optimizations of absorption and subsequent methanation processes of carbon dioxide capture, utilization, and storage
Nobuo Hara, Satoshi Taniguchi, Takehiro Yamaki, Thuy Thi Nguyen, Sho Kataoka
Abstract
The bi-objective optimization of the CO 2 absorption and subsequent methanation process using monoethanolamine (MEA) was performed based on two evaluation indexes: cost and CO 2 emissions. In the evaluation boundary, the methanation-generated exothermic heat and absorption-generated reboiler heat duty were integrated using low-pressure steam. The indexes, cost and CO 2 emissions, of the whole evaluation boundary were evaluated based on the electricity demand, process water, natural gas, hydrogen, and MEA. Further, Pareto solutions were explored by combining machine learning and genetic algorithms. Notably, the bi-objective optimizations were implemented for the following two cases using different hydrogen prices and CO 2 emission factors: the present baseline and target values in the U.S. national clean hydrogen strategy (Hydrogen Shot), and 21 and 12 Pareto solutions were obtained for the present baseline and the target of Hydrogen Shot, respectively. The expenses and CO 2 emissions for the Hydrogen Shot target reduced by approximately 80% and 50%, respectively, compared with the present baseline. Thereafter, the impacts of the hydrogen price and CO 2 emission factor, as well as the process simulation design variables, were analyzed. The analyses revealed that most of the evaluation indexes (cost and CO 2 emissions) were derived from hydrogen; additionally, the methanation pressure and temperature significantly impacted the Pareto solutions. Thus, the bi-objective optimization approach implemented here is promising for the optimizations of subsequent utilization processes. • Bi-objective optimization of CO 2 absorption and subsequent methanation was performed. • CO 2 emissions were evaluated based on life cycle assessment (LCA). • The Cost was evaluated, including operating and capital costs. • Pareto solutions were explored by machine learning and genetic algorithms. • Impacts of hydrogen price and CO 2 emission factor on Pareto solutions were clarified.