Artificial neural network (ANN) approach for modelling of arsenic (III) biosorption from aqueous solution by living cells of Bacillus cereus biomass

Giri, Anil Kumar; Patel, Raj Kishore; Mahapatra, Siba Sankar

doi:10.1016/j.cej.2011.09.111

Cited by 152 publications

(74 citation statements)

References 38 publications

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“…This model has the ability to learn from existing data and adopt to map a set of input parameters into a set of output parameters, without knowing the intricate relationship among them. ANN can be trained to identify patterns and extract trends in imprecise and complicated non-linear data (Giri et al 2011). As adsorption is a complex non-linear process, neural network are found suitable for prediction of arsenic adsorption (removal) efficiency.…”

Section: Artificial Neural Network (Ann) Modelingmentioning

confidence: 99%

Removal of arsenic(III) and arsenic(V) on chemically modified low-cost adsorbent: batch and column operations

et al. 2013

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Batch and column operations were performed utilizing thioglycolated sugarcane carbon (TSCC), a lowcost adsorbent, to remove As(III) and As(V) from aqueous systems. Under optimized batch conditions, the TSCC could remove up to 92.7 and 91.4 % for As(III) and As(V), respectively. An artificial neural network model showed the validity of TSCC as a preferable adsorbent for arsenic [As(III) and As(V)] removal in batch studies. In column operations, removal efficiency increases with increase in influent arsenic concentration and adsorbent dose and decreases with increase in flow rate. At an adsorbent dose of 6.0 g, flow rate 3.0 mL min -1 , and initial arsenic concentration 1,500 lg L -1 , the arsenic uptake capacity of TSCC for As(III) and As(V) was found to be 85.01 and 83.82 lg g -1 , respectively. The Thomas model was used to analyze the column experimental data. Results from the column operations indicated that the adsorption behavior of arsenic [As(III) and As(V)] fits exceptionally well with the Thomas model with high correlation coefficient and very low standard error. Examinations of scanning electron microscopy and FTIR spectroscopy reveal that high arsenic adsorption favors surface complexation on the adsorbent surface.

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Section: Artificial Neural Network (Ann) Modelingmentioning

confidence: 99%

Removal of arsenic(III) and arsenic(V) on chemically modified low-cost adsorbent: batch and column operations

et al. 2013

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“…Wavenumber 1231.628 cm −1 shifted to 1237.209 cm −1 and 1240.024 cm −1 shifted to 1237.829 cm −1 assigned for -SO 3 stretching for the biosorption/bioaccumulation of As(III) and As(V), respectively. The peaks at 1082.1705 cm −1 (As(III)) and 1087.132 cm −1 As(V) may be attributed to the C-N stretching vibrations of amino groups which shifted to higher frequency and appeared at 1113.178 and 1114.671 cm −1 , respectively, due to the interaction of nitrogen from the amino group with As(III) and As(V) ions [66]. Figure 9a, b exhibits the morphology of As(III) and As(V) acclimatized living bacterial cells of B. arsenicus MTCC 4380, respectively.…”

Section: Ft-ir Analysismentioning

confidence: 99%

Biosorptive Performance of Bacillus arsenicus MTCC 4380 Biofilm Supported on Sawdust/MnFe2O4 Composite for the Removal of As(III) and As(V)

Podder

Majumder

2016

Water Conserv Sci Eng

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One of the main environmental concerns of today is the occurrence of arsenic in wastewater. Targeting a solution for this problem, several efforts have been made towards research and application of cost-effective and effortlessly adaptable processes. The ability of a biofilm of Bacillus arsenicus MTCC 4380 supported on sawdust/MnFe 2 O 4 composite to biosorb/ bioaccumulate As(III) and As(V) was investigated in batch experiments. Optimum biosorption/bioaccumulation conditions were determined as a function of contact time and temperature. The equilibrium was achieved after about 220 min at 30°C temperature. Nonlinear regression analysis was done to determine the best-fit kinetic model based on three correlation coefficients and three error functions and also to predict the parameters involved in kinetic models. The results showed that both Brouers-Weron-Sotolongo and Avrami models for both As(III) and As(V) were capable of providing realistic explanation of biosorption/bioaccumulation kinetic. Applicability of mechanistic models showed that the rate controlling step in the biosorption/bioaccumulation of both As(III) and As(V) was film diffusion rather than intraparticle diffusion. The estimated thermodynamic parameters ΔG 0 , ΔH 0 , and ΔS 0 revealed that biosorption/bioaccumulation of both As(III) and As(V) was feasible, spontaneous, and exothermic under examined conditions. The activation energy (E a ) estimated from Arrhenius equation specified the nature of biosorption/bioaccumulation being ion exchange type. The effect of co-existing ions such as Cu 2+ , Zn 2+ , Bi 3+ , Cd 2+ , Fe 3+ , Pb 2+ , Co 2+ , Ni 2+ , Cr 6+ , and SO 4 2− at different concentrations was inspected.

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“…Each neuron is linked to all the neurons in the next layer. The architecture of network is exhibited as l, m, n where l neurons are exhibited at input layer (equal to the number of inputs in the network), m neurons are exhibited at the hidden layer (optimized through experimentation) and n neurons are exhibited at the output layer dependent onto number of outputs preferred from model (Rene et al 2009;Giri et al 2011). The hidden layers allow these networks for computing intricate relations between inputs and outputs.…”

Section: Modelling Technique Artificial Neural Network Modellingmentioning

confidence: 99%

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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Artificial neural network (ANN) approach for modelling of arsenic (III) biosorption from aqueous solution by living cells of Bacillus cereus biomass

Cited by 152 publications

References 38 publications

Removal of arsenic(III) and arsenic(V) on chemically modified low-cost adsorbent: batch and column operations

Removal of arsenic(III) and arsenic(V) on chemically modified low-cost adsorbent: batch and column operations

Biosorptive Performance of Bacillus arsenicus MTCC 4380 Biofilm Supported on Sawdust/MnFe2O4 Composite for the Removal of As(III) and As(V)

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

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