Asphaltenes adsorption prediction onto clay minerals: A comparison between machine learning and experimental adsorption isotherms

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 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
6 different clay minerals) was used for the modeling. To improve 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 of 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 (R2) values of 6.72 and 0.93 we obtained, respectively. Finally, the developed
MLP model was compared with well-known adsorption isotherms of Langmuir and Freundlich
and exhibited superior performance.