NDLI: Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches.

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Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Singh, Kunwar P. Gupta, Shikha Ojha, Priyanka Rai, Premanjali
Description	Country affiliation: India Author Affiliation: Singh KP ( 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)
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.
ISSN	09441344
Issue Number	4
Volume Number	20
e-ISSN	16147499
Journal	Environmental Science and Pollution Research
Language	English
Publisher	Springer
Publisher Date	2013-04-01
Publisher Place	Germany
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial Intelligence Charcoal Chemistry Chlorophenols Cocos Models, Chemical Water Pollutants, Chemical Water Purification Methods Adsorption Fruit Hydrogen-ion Concentration Kinetics Models, Statistical Neural Networks (computer) Nonlinear Dynamics Temperature Evaluation Studies Journal Article Discipline Environmental Science Discipline Environmental Chemistry
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Health, Toxicology and Mutagenesis Pollution Medicine

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