A novel machine learning framework for designing high-performance catalysts for production of clean liquid fuels through Fischer-Tropsch synthesis

Abstract

Fischer-Tropsch synthesis (FTS) attracts great interest as a sustainable route for production of sustainable transportation fuels. Catalyst design and operational conditions tune the selectivity of FTS to liquid fuels, which can be predicted with machine learning models (ML). Herein, a comprehensive dataset including 27 key input features related to the catalyst structure (with the focus on carbon supports), preparation, activation, and FTS operating conditions were investigated to predict the CO conversion and C5+ selectivity using three ML algorithms. Feature engineering and selection were implemented to identify the significant catalyst formulation descriptors. Principal component analysis was used to explore the information space and decrease the dimension of the dataset before ML prediction. In addition to the well-known effects of the operational conditions on FTS, roles of physico-chemical properties of the carbon materials were also extensively analyzed. Random forest (RF) including 4 principal components, indicated the highest accuracy for prediction of the CO conversion and C5+ selectivity with R2 of 0.91 and 0.97, respectively. The proposed framework can provide a reliable strategy in designing efficient carbon supported catalysts for FTS and guide experiments by identifying the key descriptors in the catalyst formulation and operating conditions to enhance the selectivity of liquid fuels.