Análisis descriptivo, predictivo y causal de la síntesis termocatalítica de CH3OH a partir de CO2 e H2 mediante el empleo de inteligencia artificial para proveer nuevas perspectivas sobre el proceso.

Abstract

Thermocatalytic hydrogenation of CO2 to methanol is a promising technology in the fight against climate change. This process helps to reduce CO2 emissions by transforming it into chemical compounds such as methanol, which is considered an efficient fuel and serves as a precursor in chemical synthesis. To improve the process, new materials and operating conditions are required that can be made available within reasonable time frames, so the use of artificial intelligence for this purpose can provide notable advantages. In this context, this study allowed for a descriptive, predictive and causal analysis of the thermocatalytic synthesis of methanol. For this purpose, a database made up of 3,011 experimental points obtained through the review of 160 scientific articles. The descriptive analysis revealed that the process is thermodynamically restricted, so it depends on both the reaction conditions and the influence of the catalyst. For predictive analysis of methanol space time yield (STY) from experimental descriptors, five artificial intelligence algorithms were evaluated (Random Forest, XGBoost, Neural Networks, k-Nearest Neighbors, and Supporting Vector Machines). The XGBoost and Random Forest algorithms obtained the highest cross-validation coefficients of 0.881 ± 0.013 and 0.862 ± 0.014 respectively. Once the SHAP algorithm was applied, it was identified that the most important descriptors in XGBoost and Random Forest were gas hourly space velocity (GHSV), pressure (P) and reaction temperature (T).