Design and Optimization of Solar Green Methanol Production System Based on NSGA-II and AHP-TOPSIS Method
Year: 2026
Type: article
Abstract: Electrochemical reduction of carbon dioxide (CO2RR) to methanol represents a promising approach for sustainable methanol production. Despite this potential, current technological limitations constrain both economic viability and environmental benefits. This research introduces a solar-driven multigeneration system that integrates CO2RR to enable the coproduction of electricity and green methanol. A comprehensive energy integration analysis was conducted, alongside a combined techno-economic, energy-efficiency, and environmental (3E) assessment. Multiobjective optimization was conducted using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). For solution selection, the analytic hierarchy process (AHP) was integrated with the order preference by similarity to ideal solution (TOPSIS) methodology. Results indicate that the integrated system achieves a 4.2% reduction in total utility consumption. The optimal levelized cost of methanol (LCOM), net specific carbon emissions (NetSCE), and energy efficiency (ηEN) are USD 0.526/kg, −1.16 kg CO2SCE/kg CH3OH, and 6.52%, respectively. LCOM decreases by 30.6% compared to the initial system, NetSCE increases by 3.44%, and ηEN improves by 5.84%. Under optimal operating conditions, CH3OH production capacity and grid power consumption reach 45.27 tons/day and 475.83 MWh/day, respectively. The system does not currently meet the commercial threshold and becomes economically viable only if the electricity price exceeds USD 0.223/kWh. This study provides a valuable reference for future research in system-level integration of CO2RR and multiobjective solution selection.
Source: Processes
DOI: 10.3390/pr14030508
Authors: Wenbo Hui, Guilian Liu
Institution: Xi'an Jiaotong University
Language: English
Subfield: Energy Engineering and Power Technology
Field: Energy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G8690093903
OpenAlex ID: W7126465789
