Studies on the removal of arsenic (III) from water by a novel hybrid material

Mandal, Sandip; Padhi, T.; Patel, Raj Kishore

doi:10.1016/j.jhazmat.2011.05.099

Cited by 51 publications

(16 citation statements)

References 32 publications

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“…Manganese can present in water in one of three basic forms i.e., dissolved, particulate and colloids, depending on pH of the solution. Nevertheless, the most common treatment techniques of manganese contaminated water is oxidation/filtration and adsorbing onto ion exchange resin [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

Adsorption Study for Removal of Mn (II) Ion in Aqueous Solution by Hydrated Ferric (III) Oxides

Puttamat¹,

Pavarajarn²

2016

IJCEA

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Section: Introductionmentioning

confidence: 99%

Adsorption Study for Removal of Mn (II) Ion in Aqueous Solution by Hydrated Ferric (III) Oxides

Puttamat¹,

Pavarajarn²

2016

IJCEA

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“…1) The surface of C. pyrenoidosa would obtain a positive charge when pH was \7.0 because of the following reaction (Mondal et al 2007;Mandal et al 2011;Ren et al 2012;Giri et al 2013 …”

Section: Resultsmentioning

confidence: 99%

“…H 2 O (6.138) a) Thus, when pH \ 7.0, the overall mechanism of As(III) or As(V) phycoremediation can be characterized in three different forms (Mandal et al 2011;Giri et al 2013):…”

Section: Resultsmentioning

confidence: 99%

“…(i) electrostatic interaction or coulombic interaction between positively charged centre (nitrogen, OH) and negatively charged As(III) or As(V) in solution (Mandal et al 2015 b) Again while the pH of the solution remains moderately in a neutral range (pH 7.0), phycoremediation of As(III) or As(V) on the neutral microalgal biomass surface can be defined by a ligand or ion exchange reaction mechanism, which is characterized as follows (Mandal et al 2011;Giri et al 2013 -on any material surface is dependent on a number of peripheral parameters such as pH, temperature, initial concentration of arsenic (either As(III) or As(V)) and the density of surface functional groups existing for coordination.…”

Section: Resultsmentioning

confidence: 99%

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Prediction of phycoremediation of As(III) and As(V) from synthetic wastewater by Chlorella pyrenoidosa using artificial neural network

Podder

Majumder

2017

Appl Water Sci

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An artificial neural network (ANN) model was developed to predict the phycoremediation efficiency of Chlorella pyrenoidosa for the removal of both As(III) and As(V) from synthetic wastewater based on 49 data-sets obtained from experimental study and increased the data using CSCF technique. The data were divided into training (60%) validation (20%) and testing (20%) sets. The data collected was used for training a three-layer feed-forward back propagation (BP) learning algorithm having 4-5-1 architecture. The model used tangent sigmoid transfer function at input to hidden layer (tansing) while a linear transfer function (purelin) was used at output layer. Comparison between experimental results and model results gave a high correlation coefficient (R allANN 2 equal to 0.99987 for both ions and exhibited that the model was able to predict the phycoremediation of As(III) and As(V) from wastewater. Experimental parameters influencing phycoremediation process like pH, inoculum size, contact time and initial arsenic concentration [either As(III) or As(V)] were investigated. A contact time of 168 h was mainly required for achieving equilibrium at pH 9.0 with an inoculum size of 10% (v/v). At optimum conditions, metal ion uptake enhanced with increasing initial metal ion concentration.

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“…A series of hybrid materials were synthesized by following the procedure according to our previous publication (Mandal et al 2011). The required pH 9 of the solutions was maintained by the addition of required amount of 1 M NH 4 OH.…”

Section: Synthesis and Characterization Of Adsorbentmentioning

confidence: 99%

Modeling of Arsenic (III) Removal by Evolutionary Genetic Programming and Least Square Support Vector Machine Models

et al. 2014

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In this study, the co-precipitation method was used to synthesize the cerium oxide tetraethylenepentamine (CTEPA) hybrid material with the variation of the molar concentration of the metal oxide. Physicochemical techniques, like FESEM, XRD, FTIR, HR-TEM and TGA-DSC, were used to characterize the hybrid material. The adsorption experiment was carried out to estimate the optimum condition for adsorption with the variation of adsorbent dose, pH of the solution, time and initial concentration of the adsorbate. These variables are further used to develop an approach for the evaluation of As(III) removal from water by using evolutionary genetic programming techniques (GP) and least square support vector models (LS-SVM). GP model was found to be the best performing model for understanding the nonlinear behavior and prediction of As(III) removal (minimum standard error GP training 0.411, GP testing 0.658). The adsorption process in this study followed second order kinetics. The experimental data were best fitted to linearly transformed Langmuir isotherm with an R 2 (correlation coefficient) value of >0.99 and having a maximum adsorption capacity of 124.8 mg/g at 25±2°C. The type of adsorption process was derived from the DubininRadushkevich isotherm model. A comparison between the model and actual experimental values gave a high correlation coefficient value (R 2 training (GP) 0.988, R 2 testing (GP) 0.977).

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Studies on the removal of arsenic (III) from water by a novel hybrid material

Cited by 51 publications

References 32 publications

Adsorption Study for Removal of Mn (II) Ion in Aqueous Solution by Hydrated Ferric (III) Oxides

Adsorption Study for Removal of Mn (II) Ion in Aqueous Solution by Hydrated Ferric (III) Oxides

Prediction of phycoremediation of As(III) and As(V) from synthetic wastewater by Chlorella pyrenoidosa using artificial neural network

Modeling of Arsenic (III) Removal by Evolutionary Genetic Programming and Least Square Support Vector Machine Models

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