Predictive Model Based on K-Nearest Neighbor Coupled with the Gray Wolf Optimizer Algorithm (KNN_GWO) for Estimating the Amount of Phenol Adsorption on Powdered Activated Carbon

Abstract

In this work, the adsorption mechanism of phenol on activated carbon from aqueous solutions was investigated. Batch experiments were performed as a function of adsorbent rate, solution temperature, phenol initial concentration, stirring speed, and pH. The optimal operating condition of phenol adsorption were: mass/volume ratio of 0.6 g.L−1, temperature of 20 °C and stirring speed of 300 rpm. The equilibrium data for the adsorption of phenol were analyzed by Langmuir, Freundlich, and Temkin isotherm models. It was found that the Freundlich and Temkin isotherm models fitted well the phenol adsorption on the activated carbon and that the adsorption process is favorable. The Langmuir equilibrium isotherm provides a maximum adsorption of 156.26 mg.g−1 at 20 °C. The pseudo-first-order, pseudo-second-order, intraparticle diffusion, and Boyd models were used to fit the kinetic data. The adsorption kinetics data were well described by the pseudo-second-order model. The kinetic was controlled by the external diffusion by macropore and mesopore, as well as by the micropore diffusion. The thermodynamic study revealed the exothermic and spontaneous nature of phenol adsorption on activated carbon with increased randomness at the solid-solution interface. On the other hand, a very large model based on the optimization parameters of phenol adsorption using k-nearest neighbor coupled with the gray wolf optimizer algorithm was launched to predict the amount of phenol adsorption. The KNN_GWO model showed an advantage in giving more precise values related to very high statistical coefficients (R = 0.9999, R2 = 0.9998 and R2adj = 0.9998) and very low statistical errors (RMSE = 0, 0070, MSE = 0.2347 and MAE = 0.2763). These advantages show the efficiency and performance of the model used.