A new approach in adsorption modeling using random forest regression, Bayesian multiple linear regression, and multiple linear regression: 2,4-D adsorption by a green adsorbent

Abstract

Abstract The mathematical model's usage in water quality prediction has received more interest recently. In this research, the potential of random forest regression (RFR), Bayesian multiple linear regression (BMLR), and multiple linear regression (MLR) were examined to predict the amount of 2,4-dichlorophenoxy acetic acid (2,4-D) elimination by rice husk biochar from synthetic wastewater, using five input operating parameters including initial 2,4-D concentration, adsorbent dosage, pH, reaction time, and temperature. The equilibrium and kinetic adsorption data were fitted best to the Freundlich and pseudo-first-order models. The thermodynamic parameters also indicated the exothermic and spontaneous nature of adsorption. The modeling results indicated an R2 of 0.994, 0.992, and 0.945 and RMSE of 1.92, 6.17, and 2.10 for the relationship between the model-estimated and measured values of 2,4-D removal for RFR, BMLR, and MLR, respectively. Overall performances indicated more proficiency of RFR than the BMLR and MLR models due to its capability in capturing the non-linear relationships between input data and their associated removal capacities. The sensitivity analysis demonstrated that the 2,4-D adsorption process is more sensitive to initial 2,4-D concentration and adsorbent dosage. Thus, it is possible to permanently monitor waters more cost-effectively with the suggested model application.