Computational modeling of Hg/Ni ions separation via MOF/LDH nanocomposite: Machine learning based modeling

• Published in: Arabian journal of chemistry Vol. 15; no. 12; p. 104261
• Main Authors: Ibrahim, Munjed M., Alnuwaiser, Maha Abdallah, Elkaeed, Eslam B., Kotb, Hossam, Alshehri, Sameer, Abourehab, Mohammed A.S.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2022; Elsevier
• Subjects: Artificial intelligence; Common pollutants; Nanocomposite material; Water treatment; Common pollutants; Water treatment; Nanocomposite material; Artificial intelligence
• ISSN: 1878-5352; 1878-5379
• DOI: 10.1016/j.arabjc.2022.104261

Nowadays, sustainable supplement of water has recently been identified as a vital necessity due to the existence of limited drinkable water sources. To do this, various techniques are being developed to remove various types of pollutants from water/wastewater sources. Adsorption of common water pollutants using nanocomposite materials has been of great popularity in recent years due to its high efficiency. This paper aims to develop various models based on machine learning approach to study their efficiency on predicting the experimentally measured results of Hg/Ni ions removal from water sources. To do this, this study attempts regression on a small data set using two parameters as inputs and two parameters as outputs. In this dataset, the inputs are Ion and C0, and the outputs are Ce and Qe. AdaBoost (Adaptive Boosting), a well-known ensemble method, was applied on top of three different models, including Decision Tree Regression (DT), Gaussian Process Regression (GPR), and Linear Regression (LR). After fine-tuning their hyper-parameters, the optimized model was evaluated through various metrics. For example, the R2 for ADA + GPR model has a score of 0.998 for Ce and 0.999 for Qe as the best model among these three models. This model in RMSE is the best and illustrates 0.1512 and 1.490 for Ce and Qe as error. Eventually, ADA + GPR has been selected as the optimized model with optimized dataset: (Ion = Ni, C0 = 250, Ce = 206.0). But for Qe, different amounts are illustrated: (Ion = Hg, C0 = 106.7, Ce = 577.35)