Prediction of asphaltene adsorption capacity of clay minerals using machine learning

Abstract A thorough understanding of asphaltene adsorption on clay minerals is particularly important in oil production and contaminated soil remediation using clay‐based adsorbents. In this paper, we introduced a machine learning approach as a reliable alternative for commonly used adsorption isotherms that suffer from inherent limitations in the prediction of asphaltene adsorption onto clay minerals. Machine learning (ML) models, namely multilayer perceptron (MLP), support vector machine (SVM), decision tree (DT), random forest (RF), and committee machine intelligent system (CMIS) combined with two optimizers were used. Experimental data (142 data points for six different clay minerals) was used for the modelling. To improve the accuracy of the smart models, a comprehensive data preparation such as outlier removal and feature selection was carried out. The results showed that relatively all the proposed models predict asphaltene adsorption on clay minerals with acceptable precision. Nevertheless, the MLP model showed superior performance compared with other models in which the overall root mean square error (RMSE) and coefficient of determination ( R 2 ) values of 6.72 and 0.93 were obtained, respectively. Finally, the developed MLP model was compared with the well‐known adsorption isotherms of Langmuir and Freundlich and exhibited superior performance.