Modeling Tetracycline Adsorption onto Blast Furnace Slag Using Statistical and Machine Learning Approaches

Abstract

Ground granulated blast furnace slag (GGBS) is a primary industrial waste product of iron production, and its improper disposal has been a serious environmental problem. This study aims to modify the GGBS using oxalic acid (GGBS-Ox) for the adsorption of tetracycline (TC) from an aqueous solution. GGBS-Ox was synthesized and characterized via FTIR, XRD SEM, XPS, BET, and DLS. The effects of process parameters, involving initial solution pH, stirring speed, and contact time, are evaluated by utilizing response surface methodology (RSM), artificial neural network (ANN), and random forest (RF) based models. The experimental results indicate that the removal efficiency of TC is significantly affected by the initial pH of the solution. The RSM, ANN, and RF models accurately simulated the experimental data, as indicated by the high coefficient of determination (R2), which was 0.98, 0.95, and 0.98, respectively. Additionally, kinetics, isotherm, and thermodynamic models were evaluated for the adsorption of TC onto GGBS-Ox. The findings of this study demonstrated the utilization of GGBS-Ox as an efficient and sustainable adsorbent for the treatment of TC and can be considered as a potential adsorbent for wastewater treatment.