Performance Assessment of Catalyst Materials for CO2 Hydrogenation to Methanol Using Explainable Machine Learning
Energy and Fuels, cilt.39, sa.21, ss.9956-9967, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 39 Sayı: 21
- Basım Tarihi: 2025
- Doi Numarası: 10.1021/acs.energyfuels.5c00792
- Dergi Adı: Energy and Fuels
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, Chimica, Compendex
- Sayfa Sayıları: ss.9956-9967
- Boğaziçi Üniversitesi Adresli: Evet
Özet
An extensive dataset containing 1547 data points from 84 published papers was constructed and analyzed using machine learning tools to assess the performance of catalyst materials (active metal and support). Random forest (RF) models were developed for the prediction of CO2 conversion and methanol selectivity; the SHAP (SHapley Additive exPlanations) analysis, accompanying the RF models, was used to determine the contributions of descriptors, including the catalyst materials, to the conversion and selectivity predictions. Association rule mining analysis (ARM) was also utilized to determine the effects of individual catalyst material and active metal-support combinations on methanol selectivity and to improve the explainability of the results further. RF models for both CO2 conversion and methanol selectivity were quite successful; the RMSE of training and testing were 2.81 (R2 = 0.87) and 3.74 (R2 = 0.74), respectively, for CO2 conversion, and they were 7.31 (R2 = 0.94) and 12.74 (R2 = 0.80) for methanol selectivity. SHAP analysis indicated that the reaction temperature, the support type, the active metal type, and the catalyst preparation methods are the most significant descriptors for both CO2 conversion and methanol selectivity; the temperature affects the conversion positively, while its effect on methanol selectivity is negative. ARM analysis for the catalyst material and preparation methods revealed that the use of Ga3Ni5, Ga, Ir, Ru, and Y improves methanol selectivity, while Nb2O5, CuBr2, In2O3-ZrO2, and ZnO-ZrO2 are the best performing supports. The use of evaporation-induced self-assembly method and precipitation was found to be better to improve methanol selectivity. The ARM results also indicate that Cu-Nb2O5, Ga-ZnO-ZrO2, Ru-In2O3, and Y-ZrO2 pairs promote high selectivity, while preparing Cu-based catalysts by precipitation and Ru-based catalyst with deposition-precipitation methods appears to be beneficial. The use of bimetallic Cu-InO2, Y-In2O3, La-In2O3, and Zn-ZrO2 catalysts seems to be also beneficial.