title: Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach.
abstract: 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 (EtOH)-mediated As(III) adsorption onto Zn-loaded pinecone (PC) biochar through batch experiments conducted under Box-Behnken design. The effect of EtOH 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 EtOH and pH on As(III) adsorption, whereas neural network revealed the stronger influence of EtOH (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 EtOH enhances As(III) adsorption over a pH range of 2 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) (10.47μg/g) is facilitated at 30.22mg C/L of EtOH with initial As(III) concentration of 196.77μg/L at pH5.8. 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.
authors.affiliation: Center for Clean Technology and Resource Recycling, University of Ulsan, Ulsan 680749, South Korea; Department of Applied Biotechnology, Sur College of Applied Sciences, Sur 411, Al Shariya, Sultanate of Oman.
authors.forename: Mohd
authors.initials: M
authors.lastname: Zafar
authors.affiliation: Department of Civil and Environmental Engineering, University of Ulsan, Ulsan 680749, South Korea.
authors.forename: N
authors.initials: N
authors.lastname: Van Vinh
authors.affiliation: Center for Clean Technology and Resource Recycling, University of Ulsan, Ulsan 680749, South Korea; Chemical Engineering Department, GMR Institute of Technology, Rajam 532127, Srikakulam Dist., India.
authors.forename: Shishir Kumar
authors.initials: SK
authors.lastname: Behera
authors.affiliation: Center for Clean Technology and Resource Recycling, University of Ulsan, Ulsan 680749, South Korea; Department of Civil and Environmental Engineering, University of Ulsan, Ulsan 680749, South Korea. Electronic address: parkhs@ulsan.ac.kr.
authors.forename: Hung-Suck
authors.initials: HS
authors.lastname: Park
mesh_terms: D000327:Adsorption; D000465:Algorithms; D001151:Arsenic / Q000032:analysis / Q000737:chemistry*; D002606:Charcoal / Q000737:chemistry*; D000431:Ethanol / Q000737:chemistry*; D006863:Hydrogen-Ion Concentration; D008956:Models, Chemical; D016571:Neural Networks, Computer; D015032:Zinc / Q000737:chemistry*
chemical_list: C540010:biochar; D002606:Charcoal; D000431:Ethanol; D015032:Zinc; D001151:Arsenic
keywords: Artificial neural network; As(III) removal; Box–Behnken design; Competitive adsorption; Ethanol; Hybrid RSM–GA optimization