Prediction of biosorption efficiency for the removal of copper(II) using artificial neural networks

Prakash, N.; Manikandan, S.; Govindarajan, Lakshmi Narasimhan; Vijayagopal, V.

doi:10.1016/j.jhazmat.2007.08.015

Cited by 91 publications

(46 citation statements)

References 11 publications

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“…The optimal levels of the significant factors and the interactions of these variables on biosorption were analyzed by using CCD [17]. A four-factor, five-level CCD with 30 runs was conducted in the optimum vicinity to locate the true optimum values of pH (X 1 ), dye concentration (X 2 ), biosorbent dosage (X 3 ) and speed of agitation (X 5 ) combining factorial points (-1, ?1), axial points (-2, ?2) and central point (0).…”

Section: Experimental Design and Data Analysismentioning

confidence: 99%

“…Recently, ANN methodologies are being used in many areas of science and engineering to solve environmental engineering problems such as chromium removal [14] and textile dye removal [15,16]. ANNs are considered as promising tool because of their simplicity toward simulation, less time required for model development than the traditional mathematical models [17], accurate prediction ability with limited numbers of experiments and identification of optimal operating conditions for the plant operator [18].…”

Section: Introductionmentioning

confidence: 99%

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Statistical optimization of biosorption of Reactive Orange 13 by dead biomass of Rhizopus arrhizus NCIM 997 using response surface methodology

Saraf¹,

Vaidya²

2015

Int J Ind Chem

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Background Increasing environmental awareness is forcing waste creators to consider new options such as biosorption for the disposal of colored wastewaters. Due to prohibitive costs of commercially available activated carbon, low-cost biosorbents with high adsorption capacities have gained increasing attention. The present investigation deals with utilization of a low-cost, fungal biosorbent of Rhizopus arrhizus NCIM 997 and optimization of conditions for the removal of Reactive Orange 13 dye from an aqueous solution using sequential statistically designed experiments.Results Plackett-Burman design with six independent variables (pH, temperature, biosorbent dosage, dye concentration, contact time and speed of agitation) was used to identify the most important factors influencing dye biosorption. Path of steepest ascent and central composite design were used to move toward the vicinity of the optimum operating conditions and to determine the optimum levels of the variables, respectively. The maximum biosorption capacity (133.63 mg/g) was obtained under the following conditions: pH 2.0, dye concentration 114 mg/L, biosorbent dosage 0.8 g/L and speed of agitation 85 rpm. Validation experiments and application of artificial neural network showed excellent correlation between predicted and experimental values. Conclusions Response surface methodology using central composite design was employed at the specified combinations of four independent significant factors identified by Plackett-Burman design. The fitted model was used to arrive at the best operating conditions to achieve a maximum response. Sequential optimization was successfully used to increase biosorption by 49.04 %. The study indicated that the fungal biosorbent was an effective and economical alternative for the removal of Reactive Orange 13 dye.

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Section: Experimental Design and Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical optimization of biosorption of Reactive Orange 13 by dead biomass of Rhizopus arrhizus NCIM 997 using response surface methodology

Saraf¹,

Vaidya²

2015

Int J Ind Chem

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“…As Redes Neurais Artificiais tem demonstrado uma técnica eficiente na modelagem termodinâmica de equilíbrio de fases (Sharma et al, 1999;Urata et al, 2002;Nguyen et al, 2007), o método também foi utilizados com sucesso por alguns autores na modelagem de biossorção, adsorção e troca-iônica (Jha e Madras, 2005;Schmitz et al, 2006;Fagundes-Klen et al, 2007;Prakash et al, 2008).…”

Section: Rede Neural Artificialunclassified

Modelagem do processo de troca iônica pela Lei da Ação das Massas e redes neurais artificiais

Canevesi¹,

Zanella²,

Martins³

et al. 2010

ete

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Resumo AbstractA Lei da Ação das Massas é geralmente empregada na modelagem dos dados experimentais de equilíbrio de processos de troca iônica. Esta metodologia é baseada na definição da constante termodinâmica de equilíbrio químico e considera as não idealidades na fase sólida e na fase aquosa. Outra alternativa para Networks. This work makes a comparison between both methodologies used on modeling of the equilibrium on ion exchange processes of the binary systems Pb 2+ -Na + ,

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“…This is known as a feed-forward neural network. Prakash et al (2008) estimated the effi ciency of ANN in terms of Copper (II) biosorption. They used chips from mango trees as adsorbents.…”

Section: Introductionmentioning

confidence: 99%

Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

Turp

2017

Archives of Environmental Protection

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One of the major problems occurring in wastewater is the extensive amount of pollution. These waters are formed by the leakage of snow and moisture in waste materials held in storage sites. High need for oxygen and heavy metal content of the wastewater cause the quality of the receiving environment to deteriorate in the long term. These problems lead to toxic and anaerobic conditions in the receiving environment, fi sh death or exposition to pollution, and deterioration in water quality when the receiving environment is used as a water supply. Therefore, wastewater must be collected before it reaches underground and surface water resources, and should be treated appropriately. The main chemical methods used for treating wastewater are coagulation-fl occulation, chemical deposition and chemical--electrochemical oxidation. Vivek et al. (2014) investigated the physical, chemical and biological methods that exist for the treatment of wastewater. It is generally diffi cult to ensure satisfactory treatment effi ciency and a high quality outfl ow of water by using only a single method. Nadaroglu et al. (2014) studied the removal of copper from aqueous solutions by using micritic limestone. They found that the maximum adsorption capacity was 237.05 mg/g for 1 hour and 1 g dosage. The experimental investigation results show that powdered micritic limestone has a high level of adsorption capacity in terms of copper ions. Adsorption data was correlated with the Langmuir and Freundlich isotherm models. It was found that the Langmuir and Freundlich isotherms fi tted well with the data. Consequently, it was determined that powdered micritic limestone can be used successfully for removing copper ions from aqueous solutions.Moradi et al. studied the adsorption of ammonium ions onto pumice as a natural and low-cost adsorbent. They found the optimum conditions for maximum removal of NH 4 + (70.3%) were found to be 100 g, 20 mg/L, 300 rpm and 180 min, for pumice dosage, initial NH 4 + ion concentration, mixing rate and contact time.For this, a combination of physical, chemical and biological methods is most commonly used. Garcia et al. (2014) explained that the most common physical methods used for the treatment of wastewater are precipitation, refi ning with air conditioning, adsorption and membrane fi ltration. In wastewaters containing heavy metals, refi ning is performed by using many different adsorbent materials. Malkoc et al. (2006) suggested that one such material is tea waste from the fi xed bed column used for removing Nickel (II) from an aqueous solution. Different initial nickel concentrations are input concentrations used in this study. The result obtained show that the adsorbent material obtained from tea factory waste is effi cient, and it is not an expensive material with regard to Nickel (II) removal. In addition, Erdogan et al. Abstract: This study investigates the estimated adsorption effi ciency of artifi cial Nickel (II) ions with perlite in an aqueous solution using artifi cial neural networks, based on 14...

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Prediction of biosorption efficiency for the removal of copper(II) using artificial neural networks

Cited by 91 publications

References 11 publications

Statistical optimization of biosorption of Reactive Orange 13 by dead biomass of Rhizopus arrhizus NCIM 997 using response surface methodology

Statistical optimization of biosorption of Reactive Orange 13 by dead biomass of Rhizopus arrhizus NCIM 997 using response surface methodology

Modelagem do processo de troca iônica pela Lei da Ação das Massas e redes neurais artificiais

Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

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