Conditions optimization and mechanistic study of chlortetracycline hydrochloride adsorption by TBAOH-MXene: Prediction results based on backpropagation neural network-genetic algorithm and response surface methodology

Abstract

MXene is extensively utilized in environmental remediation owing to its attributes of hydrophobicity and high specific surface area. Herein, backpropagation neural network-genetic algorithm (BPNN-GA) and response surface methodology (RSM) were utilized to optimize the adsorption conditions of chlortetracycline hydrochloride (CTC) employing tetrabutylammonium hydroxide intercalated niobium-based MXene (TBAOH-MXene). Compared to RSM, BPNN-GA showed higher accuracy in predicting the adsorption conditions with R2 (0.9923), χ2 (0.2300), MAE (0.3162), MSE (0.6432), MAPE (0.0038 %), and RMSE (0.8020). The maximum removal efficiency of CTC of 89.25 % occurred at an adsorbent dose of 20 mg, initial concentration of 10 mg/L, contact time of 90 min, and pH of 7 by the BPNN-GA optimization method. Based on this condition, the adsorption of CTC by TBAOH-MXene followed a pseudo-second-order and Freundlich mode, and the adsorption occurred spontaneously and endothermally. After five cycles, the removal efficiency of CTC was still as high as 75.31 %, indicating good cyclic stability of TBAOH-MXene. In conclusion, the use of BPNN-GA provides valuable guidance and introduces a new strategy for the optimization of adsorption conditions and further mechanistic investigation.