Ensemble Model Approach for Predicting the Yield of Dehydrogenation Products during the Oxidative Dehydrogenation of <i>n</i>-Butane

Efficient and selective oxidative dehydrogenation (ODH) catalysts are crucial to advance the production of valuable petrochemicals. In this study, we leverage the power of machine learning to predict dehydrogenation (DH) product yield and unravel the factors influencing the product distribution. A comprehensive data set obtained from experiments conducted in a fixed-bed reactor under varying temperatures, feed ratios (O₂/<i>n</i>-butane), and metal oxide loadings (Ni, Fe, Co, Bi, Mo, W, Zn, and Mn) on an aluminum oxide support served as the basis for model development. Three supervised machine learning models, Boosted Tree (BT), Extreme Gradient Boosting Linear (XGBL), and Support Vector Machine Radial (SVMR), were evaluated. The ensemble technique of the three models showed remarkable accuracy, with an RMSE of 1.65 and MAE of 1.14 on the test data set, and it demonstrated robust generalization capabilities by capturing 87% of the variation in DH yield. In the feature importance analysis of the selected models, Mo, Co, Ni, and W emerged as critical factors influencing the DH yield. The practical significance of these findings lies in their potential to revolutionize catalysis research and industrial applications. The ability of the ensemble model to predict DH yields opens new avenues for optimizing DH products and designing more advanced catalysts. By providing essential insights into the influential variables governing the ODH reactions, researchers can make informed decisions to achieve higher yields and efficiencies.