Abstract:A thorough understanding of asphaltene adsorption on clay minerals is particularly important in oil production and contaminated soil remediation using claybased 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), ran… Show more
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