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标题：Artificial Neural Network Modeling of Biosorptive Removal of Arsenic(V) by a Low-cost Biomass
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作者：P. Roy ; P. Roy
期刊名称：Journal of Materials and Environmental Science
印刷版ISSN：2028-2508
出版年度：2018
卷号：9
期号：12
页码：3206-3217
出版社：University of Mohammed Premier Oujda
摘要：The presence of arsenic in drinking water has been recognized as a serious communityhealth problem because of their toxic nature and therefore, its removal is highly essential.This paper deals with batch biosorption study for the removal of pentavalent arsenic ionsfrom aqueous solutions using finely ground (250 μm) Azadirachta indica (neem) barkpowder (AiBP) as a low-cost biosorbent. Employing the batch experimental setup, theeffect of operational variables such as initial concentration of As(V), pH, biosorbent dose,contact time, temperature and agitation speed on the As(V) removal process were studied.Under optimized batch conditions, the AiBP could remove up to 86.6% of As(V) fromcontaminated water. The biosorbent dose had the most significant impact on thebiosorption process. The artificial neural network (ANN) model developed from batchexperimental data sets, provided reasonable predictive performance (R2 = 0.951; 0.967) ofarsenic biosorption. The study on equilibrium biosorption of batch operation revealed thatFreundlich isotherm model gave the best fit to experimental data. The nature ofbiosorption of As(V) by AiBP was physisorption as inferred from the D–R isothermmodel. The biosorption is pseudo second–order, exothermic and spontaneous.
关键词：Arsenic; Biosorption; Low;cost biomass; Batch study; ANN modeling.