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One of the eye-catching procedures for pollution reduction is converting carbon dioxide to valuable materials and chemicals as methane, which is known as carbon dioxide methanation. Although the carbon dioxide hydrogenation process can help the environment by reducing carbon in the atmosphere, it can also release toxic emissions into the environment, which needs urgent assessment. In this research, the final goal is to determine the best path for this process in order to reach a minimum level of environmental pollution and maximum yield. Based on that, machine learning approaches including artificial neural networks and Bayesian optimization-based support vector machine kernel were employed for the carbon dioxide methanation process with Ni/Al 2 O 3 catalyst to find the first objective function, and then an environmental model was formulated based on life cycle assessment results in order to generate global warming potential. The multi-objective optimization problem, which was based on four decision parameters (temperature, pressure, hydrogen to carbon ration, and gas velocity), was computed using a genetic algorithm, and decision-making techniques were used for finding the best solution. For the hydrogen/carbon dioxide ratio, the optimal ratio was approximately 4–4.5. Regarding temperature, the range of 340–360 °C has been determined, and for the optimal gas velocity, the range of 6600–7000 L/g cat.h has been computed. The best optimal pressure obtained by the CODAS, COPRAS, MOORA, MABAC, SAW, and TOPSIS methods was 7.69 bar, and the optimal pressure value computed by other methods has been around 1–2 bar. [Display omitted] • The catalyst synthesis has the highest environmental impact due to its high energy demand. • Nickel nitrate production has the most significant impact in most of the categories. • Neural Network learning outperformed Support Vector Machine in training accuracy. • Temperature has the most significant impact on conversion based on feature importance. • The improvement in system efficiency leads to more greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Carbon dioxide methanation process is a well-known carbon dioxide utilization technology, not only on account of its ability to subside carbon dioxide in the atmosphere but also to produce methane, which is of serious industrial significance. Although this process is promising in terms of tackling greenhouse gases and global warming, it can, on the other hand, release toxic emissions into the atmosphere, rivers and soil during the process. At this point, life cycle environmental assessment emerges as a crucial tool to reveal the overall effects of this technology. This paper presents a life cycle assessment case study for carbon dioxide methanation process to evaluate all aspects of its environmental impacts. Different scenarios for this purpose were considered by changing catalyst types, namely, mixed metal oxide and zeolite-supported metal catalysts. The results showed that the toxic wastes formed and emissions released when using the Ni/Al 2 O 3 catalyst were less compared to the other cases. Not only did the change in material type in the catalyst affect the total emissions, but the catalyst conversion and selectivity had an influence on the life cycle impact of the system as well. Various power generation alternatives considering renewable and non-renewable sources were evaluated, while a combination of natural gas and wind turbines for the initial sources of power generation was found to perform better in terms of environmental impact. [Display omitted] • CO 2 methanation on Ni/Al 2 O 3 catalyst is more environmentally-friendly. • Using natural gas for CO 2 capture emits fewer pollutants than using coal. • Conversion and selectivity factors can affect LCA results of a reaction-based process. • A mixture of natural gas power plants and wind turbines can reduce environmental impacts. • Perovskite solar cells show toxic effects on climate change and metal depletion. [ABSTRACT FROM AUTHOR]