Aqueous arsenic (III) removal using a novel solid waste based porous filter media block: Traditional and machine learning (ML) approaches

Abstract

Arsenic (As) contamination of groundwater is one of the main obstacles preventing people from having easy access to potable water. Gypsum-based a porous filter media block (PFMB) made with moringa bark was proposed to remove arsenic (III) from aqueous solutions. The adsorption isotherms using Langmuir (R2 = 0.976) and Freundlich (R2 = 0.967) and kinetics using pseudo-first and second order and intraparticle diffusion models were achieved. The removal rates of PFMB using 10 g/L in batch experiments were found to be 98.4 % and 94.2 % for 0.50 and 1.0 ppm, respectively. The efficiency was evaluated by varying the material composition, pH, and experimental conditions using a machine learning model with an R2 = 0.95. A new approach to forecasting adsorption capacity based on material composition and experimental data allows the effective determinate to be found from a wider perspective. The model explanation with shapley additive value (SHAP) and partial dependence plots (PDP) showed the initial concentration, and time were the most influential factors in the adsorption process. Arsenic (III) adsorption on the innovative composite material appears promising, which will ensure the reuse and effective disposal of solid waste. ANN can be used to evaluate and predict new conditions without lab experimentation.