Developing Machine Learning Models for Catalysts in Oxidative Dehydrogenation of n-butane

1,3-butadiene is widely used as a raw material in the synthesis of rubber and automobile tires. In this study, machine learning models were utilized to predict the performance of catalysts in the oxidative dehydrogenation of n-butane to 1,3-butadiene., The datasets consisted of 304 data points generated from experiments conducted in a fixed-bed reactor in the temperature range of 400- 500 & DEG;C and O-2/C-4 molar feed ratio of 1-4 mol/mol, using metal oxides of Nickel (Ni), Iron (Fe), Cobalt (Co), Bismuth (Bi), Molybdenum (Mo), Manganese (Mn) and Tungsten (W) supported on gamma alumina catalysts. Repeated 10-fold cross-validation was used, with 70 % of the datasets randomly selected for training and optimizing the models, whereas the remaining 30 % were used for testing the models. Among the various models examined, support vector machine with radial basis function (SVMR) model had the best coefficient of determination (R-2) performance for the n-butane conversion (training set: 98.3 % vs. test set: 88.3 %) and 1,3-butadiene selectivity (training set: 94.0 % vs. test set: 88.6 %). In addition, the models had the lowest training and test mean absolute errors (MAE) and root mean square errors (RMSE). This study highlights the notable role of machine learning algorithms in enhancing the prediction of n-butane oxidative dehydrogenation catalyst performance, thus promoting the development of more effective catalyst designs that exhibit high activity and selectivity, contributing positively to the field of heterogeneous catalysis. Notably, a user-friendly application incorporating the developed machine learning models was developed, providing an invaluable tool for the real-time on-site prediction of catalyst performance in the oxidative dehydrogenation of n-butane to 1,3-butadiene. This application is a significant advancement, facilitating swift and accurate catalyst performance prediction, fostering more efficient catalyst design, streamlining, and revolutionizing the rubber and automobile tire synthesis industries.