Machine learning analysis of catalytic CO2 methanation
International Journal of Hydrogen Energy, cilt.48, sa.64, ss.24904-24914, 2023 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 48 Sayı: 64
- Basım Tarihi: 2023
- Doi Numarası: 10.1016/j.ijhydene.2022.12.197
- Dergi Adı: International Journal of Hydrogen Energy
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Artic & Antarctic Regions, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, Environment Index, INSPEC
- Sayfa Sayıları: ss.24904-24914
- Anahtar Kelimeler: Catalytic CO 2 methanation, Catalytic CO 2 hydrogenation, Machine learning, Random forest, CO 2 conversion prediction
- Boğaziçi Üniversitesi Adresli: Evet
Özet
In this work, a detailed dataset containing 4051 data points gathered from 527 distinct experiments in 100 published articles for catalytic CO2 methanation was analyzed using machine learning methods. A pre-analysis of the database was performed using simple descriptive statistics while a random forest (RF) model was developed to predict CO2 conversion as the function of 23 descriptors including catalyst properties, preparation methods, and reaction conditions. Boruta analysis was also performed to identify the significant variables. The random forest model was found to be quite successful in predicting CO2 conversion with the training and testing root mean square error (RMSE) of 6.4 and 12.7 respectively; R2 was 0.97 for training while it was 0.85 for testing. The success of the model was also verified by computing CO2 conversion profiles for individual experiments in test data and comparing them with those reported in the related papers.