Mathematical modeling of ions adsorption from water/wastewater sources via porous materials: A machine learning-based approach

Abstract

This paper developed the predictive modeling of substance concentration (C) utilizing the input parameters x and y, for analysis of adsorption process. Employing three distinct machine learning models—Multilayer Perceptron (MLP), polynomial regression (PR), and Support Vector Machine (SVM)—the study investigates the efficacy of models in capturing the relationships between the inputs and output. The models are trained from data obtained from mass transfer calculations for removal of solute from solution via porous adsorbent. Furthermore, the hyper-parameters for each model are optimized through the utilization of the Political Optimizer (PO). The Multilayer Perceptron model emerges as a standout performer, showcasing an exceptional R-squared score of 0.9981, indicative of a robust fit to the data. Complemented by impressively low MAE and MSE values (7.94043E-01 and 2.0420E+00, respectively), the MLP model attests to its ability to provide accurate predictions and discern underlying patterns within the dataset. The polynomial regression model, while slightly trailing behind the MLP in terms of R-squared score (0.95929), revealed commendable predictive performance. Support Vector Machine also proves to be a formidable contender, boasting a robust R-squared score of 0.96055.