Ethanol mediated As(Ⅲ) adsorption onto Zn-loaded pinecone biochar:Experimental investigation,modeling,and optimization using hybrid artificial neural network-genetic algorithm approach

Organic matters(OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol(EtO H)-mediated As(Ⅲ) adsorption onto Zn-loaded pinecone(PC) biochar through batch experiments conducted under Box–Behnken design. The effect of EtO H on As(Ⅲ) adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtO H and pH on As(Ⅲ) adsorption,whereas neural network revealed the stronger influence of Et OH(64.5%) followed by pH(20.75%)and As(Ⅲ) concentration(14.75%) on the adsorption phenomena. Besides, the interaction among process variables indicated that Et OH enhances As(Ⅲ) adsorption over a pH range of2 to 7, possibly due to facilitation of ligand–metal(Zn) binding complexation mechanism.Eventually, hybrid response surface model–genetic algorithm(RSM–GA) approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As(Ⅲ)(10.47 μg/g) is facilitated at 30.22 mg C/L of Et OH with initial As(Ⅲ) concentration of 196.77 μg/L at pH 5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As(Ⅲ) species in the presence of OM.