Machine learning aided catalyst activity modelling and design for direct conversion of CO2 to lower olefins

In the recent years, the demand for utilisation of CO2 into different chemicals has gathered interest due to increased concerns for global warming. The current work focuses on development of machine learning (ML) framework for catalyst modelling and design for direct conversion of CO2 to lower olefins (LO) based on the structural-composition-operating parameters. Comprehensive review, and data mining exercise was carried out and data base was formed from -55 relevant reports, including 18 input parameters and catalyst activity (i.e., CO2 conversion (%) & LO selectivity (%)) as output parameter. Artificial neural network (ANN) models were developed using Bayesian-Regularisation (BR) and Levenberg-Marquardt (LM) backpropagation learning algorithms for prediction of catalyst activity. Performance of the developed ANN models are compared with linear, tree-based, and kernel-based ML models and has been evaluated based on statistical measures. Out of these ML models, ANN-BR is able to predict CO2 conversion & LO selectivity with less deviation from experimental data (R = 0.90 & 0.8, RMSE = 8.43 & 16.73, AAD = 5.8 & 9.5 for test data respectively), compared to other ML models. Input contribution on post analysis of modelling is considered to understand the significance of predominate feature affecting the target variables. Further, integrated catalyst and process design carried out using inverse design based on multi-objective optimization (NSGA-II) with ANN-BR as objective function. Results indicate two-to-three-folds increase in yields with optimal catalyst composition, operating conditions, and novel combination of catalysts for efficient conversion of CO2 to lower olefins compared to reported experimental results.