The use of artificial neural networks (ANN) for modeling of adsorption of Cu(II) from industrial leachate by pumice

Turan, Nurdan Gamze; Mesci, Başak; Özgönenel, Okan

doi:10.1016/j.cej.2011.05.005

Cited by 170 publications

(53 citation statements)

References 46 publications

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“…Actually an artificial neural network (ANN) is an enormously interconnected network structure comprising of several simple processing elements proficient of executing parallel computation for data processing. This technique is valuable where the complication of the mechanisms indicating performance of process is very high (Turan et al 2011a(Turan et al , 2011b.They comprise a chain of mathematical correlation which are utilized for simulating the learning and memorizing operation. ANNs learn by example in which an actual measured input variables set and analogous outputs are offered for determining the guidelines that manage the relationship between the variables (Chairez et al 2009).…”

Section: Modelling Technique Artificial Neural Network Modellingmentioning

confidence: 99%

“…The LevenbergMarquardt back propagation algorithm which was independently developed by Levenberg (1944) and Marquardt (1963), (LMA) was applied for training of the network as the best algorithm. Total iteration number was set at 3000 for all learning algorithms and the performance goal is set at 10 -5 (Turan et al 2011b). The number of nodes in the hidden layer (H) is described by the relation as follows (Giri et al 2011;Mandal et al 2015):…”

Section: Ann Softwarementioning

confidence: 99%

“…Few investigators tried to develop an assessment of ANN as one valuable approach for predicting and simulating the removal process. Examples for application of AANs in water treatment are removal of arsenic by the modification of solid waste vegetable oil industry with Fenton reagent (FMSWVOI) (Jaafarzadeh et al 2012), removal of Zn(II) ions from leachate by hazelnut shell (Turan et al 2011a), adsorption of Cu(II) from industrial leachate by pumice (Turan et al 2011b) and removal of lead ions by Nigella sativa seeds (black cumin) (Bingöl et al 2012). ANN methods were compared because of their generalization and predictive abilities, sensitivity analysis as well as optimization efficiency in metal ion removal from wastewater (Turan et al 2011a, b;Bingöl et al 2012).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Modelling Technique Artificial Neural Network Modellingmentioning

confidence: 99%

Section: Ann Softwarementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Heavy metals are persistent and unbiodegradable in environment and can be accumulated in aquatic organisms (Turan et al, 2011). Cadmium is used as a raw material in processes such as metallurgy, cadmium -nickel batteries production, oil dyes, mining, stabilizers and alloys.…”

Section: Introductionmentioning

confidence: 99%

Cadmium Removal by using Pumice Modified with Iron Nanoparticles from Aqueous Solutions

2016

Global NEST Journal

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In this study, the removal of cadmium by using pumice coated with iron nanoparticles (INPs) from synthetic wastewater was investigated. The effects of parameters influencing adsorption: contact time (10-20 min), pH (3-9), initial cadmium concentration (25-125 mg l ) were studied. The pumice stone used in this research was, first, crashed and then sieved with 20 mesh standard sieves (0.85 mm); finally, it was coated with INPs. An atomic absorption spectrophotometer was used to measure cadmium contents and isotherm models and adsorption kinetics were studied. The results showed the adsorption process of cadmium reached equilibrium at contact time of 80 min. With increasing pH solution, the efficiency enhanced which peaked at pH 7-8. Cadmium concentration increase resulted in a decrease in efficiency, whereas adsorbent dose increase improved it. Equilibrium data of adsorption followed isotherms models: Langmuir and Freundlich. The highest removal efficiency and adsorption capacity were, respectively, 83% and 17.27 mg g -1 . Furthermore, absorption kinetics is better described by the pseudo second-order model. According to the results obtained, pumice coated with INPs is an effective adsorbent and can be introduced as a suitable option in practical applications.

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“…This is important when the effect of one variable differs depending on the level of another variable, and when the knowledge about the process itself is limited. Modeling of extraction 6,8,11,20 , adsorption [25][26][27] , synthesis 28 processes etc., using experimental design were commonly described in the literature. Thus, for instance, Pingret et al 29 optimized production of antioxidant-rich extracts from apple pomace using ultrasound-assisted extractions, while Chua et al 30 optimized the extraction conditions of phospholipids from palmpressed fiber.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Quercetin Extraction from Green Tea ( Camellia sinensis ) Using Central Composite Design, and the Pharmacological Activity of the Extract

Savić

Nikolić

et al. 2016

Chem.Biochem.Eng.Q.

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The aim of this paper was to optimize an extraction procedure of quercetin from green tea using central composite design. Extraction time, ethanol concentration, and solid to liquid ratio were selected as the independent variables, while quercetin yield was defined as a response. The impact of factors and their interactions on the quercetin yield was studied based on the results of ANOVA test. The extraction time of 58.5 min, ethanol concentration of 94.7 % (v/v), and solid to liquid ratio of 1:19.4 (m/v) were found as the optimal conditions. The experimental confirmation of the proposed optimal conditions indicated that there was a good agreement between the experimental and predicted values. In addition to quercetin, the presence of 17 bioactive compounds was confirmed in the green tea extract using mass spectrometry method. Antioxidant, antimicrobial and antitumor activity of the optimal extract was determined using DPPH assay, disk diffusion method, and MTT assay, respectively.

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The use of artificial neural networks (ANN) for modeling of adsorption of Cu(II) from industrial leachate by pumice

Cited by 170 publications

References 46 publications

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

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

Cadmium Removal by using Pumice Modified with Iron Nanoparticles from Aqueous Solutions

Optimization of Quercetin Extraction from Green Tea ( Camellia sinensis ) Using Central Composite Design, and the Pharmacological Activity of the Extract

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