title: Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches.
abstract: The research aims to develop artificial intelligence (AI)-based model to predict the adsorptive removal of 2-chlorophenol (CP) in aqueous solution by coconut shell carbon (CSC) using four operational variables (pH of solution, adsorbate concentration, temperature, and contact time), and to investigate their effects on the adsorption process. Accordingly, based on a factorial design, 640 batch experiments were conducted. Nonlinearities in experimental data were checked using Brock-Dechert-Scheimkman (BDS) statistics. Five nonlinear models were constructed to predict the adsorptive removal of CP in aqueous solution by CSC using four variables as input. Performances of the constructed models were evaluated and compared using statistical criteria. BDS statistics revealed strong nonlinearity in experimental data. Performance of all the models constructed here was satisfactory. Radial basis function network (RBFN) and multilayer perceptron network (MLPN) models performed better than generalized regression neural network, support vector machines, and gene expression programming models. Sensitivity analysis revealed that the contact time had highest effect on adsorption followed by the solution pH, temperature, and CP concentration. The study concluded that all the models constructed here were capable of capturing the nonlinearity in data. A better generalization and predictive performance of RBFN and MLPN models suggested that these can be used to predict the adsorption of CP in aqueous solution using CSC.
authors.affiliation: Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research (Council of Scientific and Industrial Research), Post Box 80, MG Marg Lucknow 226 001, India. kpsingh_52@yahoo.com
authors.forename: Kunwar P
authors.initials: KP
authors.lastname: Singh
authors.forename: Shikha
authors.initials: S
authors.lastname: Gupta
authors.forename: Priyanka
authors.initials: P
authors.lastname: Ojha
authors.forename: Premanjali
authors.initials: P
authors.lastname: Rai
mesh_terms: D000327:Adsorption; D001185:Artificial Intelligence; D002606:Charcoal / Q000737:chemistry*; D002733:Chlorophenols / Q000737:chemistry*; D003059:Cocos / Q000737:chemistry*; D005638:Fruit / Q000737:chemistry; D006863:Hydrogen-Ion Concentration; D007700:Kinetics; D008956:Models, Chemical; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013696:Temperature; D014874:Water Pollutants, Chemical / Q000737:chemistry*; D018508:Water Purification / Q000379:methods*
chemical_list: D002733:Chlorophenols; D014874:Water Pollutants, Chemical; D002606:Charcoal; C030683:2-chlorophenol