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Article

A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms

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Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia
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Department of Chemical Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia
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Department of Civil Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia
*
Author to whom correspondence should be addressed.
Water 2020, 12(12), 3490; https://doi.org/10.3390/w12123490
Received: 16 November 2020 / Revised: 6 December 2020 / Accepted: 9 December 2020 / Published: 11 December 2020
(This article belongs to the Section Wastewater Treatment and Reuse)
Applications of machine learning algorithms (MLAs) to modeling the adsorption efficiencies of different heavy metals have been limited by the adsorbate–adsorbent pair and the selection of specific MLAs. In the current study, adsorption efficiencies of fourteen heavy metal–adsorbent (HM-AD) pairs were modeled with a variety of ML models such as support vector regression with polynomial and radial basis function kernels, random forest (RF), stochastic gradient boosting, and bayesian additive regression tree (BART). The wet experiment-based actual measurements were supplemented with synthetic data samples. The first batch of dry experiments was performed to model the removal efficiency of an HM with a specific AD. The ML modeling was then implemented on the whole dataset to develop a generalized model. A ten-fold cross-validation method was used for the model selection, while the comparative performance of the MLAs was evaluated with statistical metrics comprising Spearman’s rank correlation coefficient, coefficient of determination (R2), mean absolute error, and root-mean-squared-error. The regression tree methods, BART, and RF demonstrated the most robust and optimum performance with 0.96 ⫹ R2 ⫹ 0.99. The current study provides a generalized methodology to implement ML in modeling the efficiency of not only a specific adsorption process but also a group of comparable processes involving multiple HM-AD pairs. View Full-Text
Keywords: artificial intelligence; regression; statistical analysis; ten-fold-cross-validation; adsorbent; removal efficiency artificial intelligence; regression; statistical analysis; ten-fold-cross-validation; adsorbent; removal efficiency
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MDPI and ACS Style

Hafsa, N.; Rushd, S.; Al-Yaari, M.; Rahman, M. A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms. Water 2020, 12, 3490. https://doi.org/10.3390/w12123490

AMA Style

Hafsa N, Rushd S, Al-Yaari M, Rahman M. A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms. Water. 2020; 12(12):3490. https://doi.org/10.3390/w12123490

Chicago/Turabian Style

Hafsa, Noor, Sayeed Rushd, Mohammed Al-Yaari, and Muhammad Rahman. 2020. "A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms" Water 12, no. 12: 3490. https://doi.org/10.3390/w12123490

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