Multi-objective optimization of CO₂ emission and thermal efficiency for on-site steam methane reforming hydrogen production process using machine learning

Currently, hydrogen is produced primarily through steam methane reforming, a gray hydrogen production process that generates CO₂ as a by-product. Thus, it is crucial to optimize the process thermal efficiency with minimizing CO₂ generation in a hydrogen production process. This study focuses on the multi-objective optimization of low-carbon hydrogen production process, considering both process thermal efficiency maximization and CO₂ emission minimization. To this end, a hybrid deep neural network model is developed to increase the robustness of the multi-objective optimization. The developed hybrid deep neural network model is incorporated into a proposed multi-objective particle swarm optimization algorithm that performs Pareto dominance-based multi-objective optimization. In experiments conducted, Pareto-optimal solutions with thermal efficiency distribution between 77.5 and 87.0% and CO₂ emissions between 577.9 and 597.6 t/y were obtained. Furthermore, the Pareto-optimal front was analyzed to provide various representative solutions to assist decision-makers. The findings of this study can enable efficient and flexible process operations according to various requirements.