Operational parameter impact and back propagation artificial neural network modeling for phosphate adsorption onto acid-activated neutralized red mud

Abstract In this research the combination of neutralization activation and acid activation processes was employed to improve the physicochemical characters of red mud. In order to better understand the phosphate adsorption behaviors and further improve the phosphate adsorption performance of acid-activated neutralized red mud (AaN-RM), for the first time the impact of operational parameters on phosphate adsorption onto AaN-RM was systematically investigated, and back propagation artificial neural network (ANN) modeling was conducted. The results demonstrated that phosphate adsorption capacity of AaN-RM decreased with the enhancement of adsorbent dosage and the concentration of the competing ion (carbonate), while it increased with the increase of initial phosphate concentration and contact time. The optimal adsorption temperature and initial solution pH for phosphate adsorption onto AaN-RM were 50 °C and 4.0, respectively. Moreover, a 6-10-1 feed forward ANN structure with trainlm algorithm was successfully constructed for predicting the phosphate removal by AaN-RM. The RMSE and R 2 values for two subsets (training and validation subset, and testing subset) were 3.06 and 2.61, and 0.9932 and 0.9969, respectively. Furthermore, the importance analysis showed that contact time and initial phosphate concentration were the most influential parameters on phosphate removal by AaN-RM, the importance of which reached 24.64% and 22.16%, respectively.