preprints.org > engineering > chemical engineering > doi: 10.20944/preprints202309.0539.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 6 September 2023 / Approved: 7 September 2023 / Online: 8 September 2023 (04:20:03 CEST)

Among various processes, catalytic CO2 methanation has emerged as a promising method for carbon capture and utilization. Therefore, a CO2 methanation reaction studied with experimentally. The extent of coke formation on the catalyst after the reaction was quantitatively studied. Then, four different machine learning model were employed in order to model CO2 methanation process and to predict CO2, CO, CH4, H2 and N2 concentrations at outlet. The results of the machine learning models were consistent with the experimental data. Performances of the proposed regression methods are evaluated with 10-fold cross validation. Random forest and decision tree regression performed the best among other methods by achieving R >0.98 for the output concentrations and were able to outperform other modeling approaches. It was shown that the best result among the literature studies is reached in this study. The results have demonstrated the possibility of simulation of the methanation process via machine learning techniques. These techniques can be used control and optimization of carbon capture.

methane; carbon dioxide; modelling; artifical intelligence

Engineering, Chemical Engineering

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