A principal component analysis assisted machine learning modeling and validation of methanol formation over Cu-based catalysts in direct CO2 hydrogenation

With the advent of powerful machine learning algorithms strongly supporting complex non-linear regression modeling, catalyst design features for a wide and customized set of catalysts used for a specific reaction have been made easy. Herein, we use these techniques in the methanol synthesis by CO2 reduction over Cu-based binary and ternary catalysts with the help of three machine learning algorithms: artificial neural network, support vector machine regression, and gaussian process regression. 227 catalytic performance dataset points from existing literature on CO2 hydrogenation to methanol were compiled and initially accessed by Principal Component Analysis (PCA) for training and preliminary evaluation of the algorithms, which was further guided using a 10-fold cross-validation method. The predictive model and its insights were validated experimentally over 30 datasets derived from experimental runs of this reaction over a ternary Cu/ZnO/ZrO2 laboratory synthesized catalyst at varying conditions of temperature, pressure, and space velocity in a continuous mode fixed-bed plug-flow reactor. The assessment of the space of input and laboratory data was aided by Principal Component Analysis (PCA), scores, and loadings plot. This work shows how experimentalists can predict typical heterogeneous catalytic reaction outputs (R2 greater than 0.9 for three variables) with fair accuracy using a combination of machine learning and PCA.