Adsorptive removal of humic substances using cationic surfactant-modified nano pumice from water environment: Optimization, isotherm, kinetic and thermodynamic studies

In this study, nano pumice (NP) and a cationic surfactant (hexadecyltrimethylammonium-chloride (HDTMA.Cl)) treated nano pumice (HMNP) were used for humic acid (HA) adsorption from an aqueous solution. The adsorption process was modeled and optimized using Response surface methodology-central composite design (RSM-CCD) and Artificial neural networks-Genetic algorithm (ANN-GA). The results show that the ANN model outperforms the RSM-CCD model in terms of response prediction. Optimization results based on the RSM-CCD approach proposed pH 3, adsorbent dose 3 g L-1, reaction time 60 min, and initial HA concentration 5 mg L-1 as optimal points of the variables, to reach the maximum adsorption efficiency of 100% and 65.4% by HMNP and NP adsorbents. The maximal adsorption capacity of NP was 1.21 mg g- 1, while that of HMNP was 27.34 mg g-1. The optimal points of process parameters by the ANN-GA method are in accordance with the values sug-gested by the RSM-CCD method. In isotherm studies, Langmuir model was found to be the best-fitted model for both adsorbent with R2 = 0.97 for NP and 0.992 for HMNP, and also among three different kinetic models which were assessed, Pseudo-second-order model with R2 = 0.9989 for HMNP and R2 = 0.9957 for NP were the best- fitted models for HA removal. Thermodynamic studies indicated that the HA adsorption process by both of the adsorbents is endothermic and the nature of HMNP was found spontaneous while for NP was non-spontaneous. The value of & UDelta;H for both adsorbents was in the range of 34-36.8 kJ mol- 1 so the process is clarified as chemical-physical adsorption. The reusability test revealed that the adsorption effectiveness of HMNP drops from 100% to 82.4% after 10 consecutive recycles. The influence of interfacing anions indicated that the adsorption efficiency drops from 100% to 95.4% when the anions were added to the reaction solution.

Automated summary

This study used nano pumice and a cationic surfactant treated nano pumice for adsorption of humic acid from water. The process was modeled and optimized using response surface methodology and artificial neural networks. The results showed that the artificial neural network model outperformed the response surface methodology model in terms of response prediction.