Artificial neural network modeling in competitive adsorption of phenol and resorcinol from water environment using some carbonaceous adsorbents

Aghav, R.M.; Kumar, Sunil; Mukherjee, Somnath

doi:10.1016/j.jhazmat.2011.01.067

Cited by 152 publications

(56 citation statements)

References 22 publications

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“…Each of these layers consists of a number of inter-connected processing units called neurons. These neurons interact by sending signals to one another along weighted connections (Aghav et al 2011). Each neuron is connected to all neurons in the preceding and following layers by links.…”

Section: Experimental Designmentioning

confidence: 99%

Modeling of biosorption of Cu(II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN)

2014

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In the present work, spent tea leaves were modified with Ca(OH) 2 and used as a new, non-conventional and low-cost biosorbent for the removal of Cu(II) from aqueous solution. Response surface methodology (RSM) and artificial neural network (ANN) were used to develop predictive models for simulation and optimization of the biosorption process. The influence of process parameters (pH, biosorbent dose and reaction time) on the biosorption efficiency was investigated through a twolevel three-factor (2 3 ) full factorial central composite design with the help of Design Expert. The same design was also used to obtain a training set for ANN. Finally, both modeling methodologies were statistically compared by the root mean square error and absolute average deviation based on the validation data set. Results suggest that RSM has better prediction performance as compared to ANN. The biosorption followed Langmuir adsorption isotherm and it followed pseudo-second-order kinetic. The optimum removal efficiency of the adsorbent was found as 96.12 %.

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Section: Experimental Designmentioning

confidence: 99%

Modeling of biosorption of Cu(II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN)

2014

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“…Foi proposto que a extração destes poluentes envolve a interação entre os anéis aromáticos dos fenóis e os elétrons π da estrutura do carbono grafite do carvão ativo. 8,34 O pH pode influenciar o processo de adsorção devido aos vários grupos funcionais diferentes presentes na estrutura do carvão. Em pH baixo (<3), por exemplo, as interações entre as nuvens aromáticas dos anéis dos fenóis e da estrutura do grafite são dominantes.…”

Section: Adsorção Por Carvão Ativounclassified

“…Alguns segmentos industriais poluentes e concentrações de fenol presentes nos rejeitos A biodegradação de cloro-fenóis não é viável, pois possui uma cinética lenta e incompleta e os produtos formados são mais tóxicos ao meio ambiente e ao homem do que os precursores. 7,8 A exposição humana a estes compostos pode acarretar danos genéticos ou propiciar prejuízos para saúde, comprometendo os sistemas respiratório e digestivo, olhos, pele, cérebro, rins, coração, fígado.…”

Section: Introductionunclassified

Methods for the Removal of Phenolic Derivatives from Aqueous Effluents

Cunha¹,

Aguiar²

2014

Revista Virtual De Química

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Phenolic derivatives are compounds that do not occur naturally in water. The presence of phenols in environment is due to human activity. These compounds have been detected in effluents from different industries, such as: petrochemical, pulp and paper, pharmaceutical, textile, coal mining, coke industry, etc. The Brazilian Law establishes the value of 0.5 mg/L as maximum concentration of these derivatives in water, however in some industries the concentration can reach 2000 mg/L. This manuscript reports the main techniques used for the removal of phenolic derivatives from industrial wastewater, such as: wet air oxidation, biological degradation, use of organic or inorganic adsorbents. Additionally, some aspects about the toxicity are presented.Keywords: Phenolic derivative; adsorbent; industrial wastewater; environment. ResumoDerivados fenólicos são compostos que não ocorrem naturalmente em corpos d'água. A presença de fenóis no meio ambiente está associada à atividade humana. Estes compostos são encontrados em rejeitos de diferentes tipos de indústrias, tais como: petroquímica, papel e celulose, farmacêutica, têxtil, mineradoras, coquerias, entre outras. A legislação brasileira estabelece o valor de 0,5 mg/L como a concentração máxima destes derivados em água. Entretanto, no efluente de algumas indústrias a concentração pode alcançar 2000 mg/L. Este artigo reporta as principais técnicas utilizadas para remoção de derivados fenólicos de efluentes aquosos industriais, tais como: oxidação por ar úmido, degradação biológica, uso de adsorventes orgânicos ou inorgânicos. Adicionalmente, são apresentados alguns aspectos associados à toxidez destes poluentes.Palavras-chave: Derivados fenólico; adsorvente; efluentes industriais; meio ambiente.

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“…Hava kirliliğinden su ve toprak kirliliğine kadar birçok alanda muhtelif prosesleri öğrenip modellemek için kullanılan yapay sinir ağları [14][15][16][17][18][19][20], biyolojik atıksu arıtma proseslerinin modellenmesi amacıyla da uygulanmıştır [21][22][23]. Ne var ki biyolojik atıksu arıtma proseslerinin yapay sinir ağları ile modellenmesi amacıyla gerçekleştirilen çalışmaların çoğu ağ topolojisinin kurulması üzerine odaklanmış; ağda kullanılan aktivasyon fonksiyonları geri planda kalmıştır.…”

Section: Introductionunclassified

Bi̇yoloji̇k Atiksu Aritma Tesi̇sleri̇nde Karbon Ve Azot Gi̇deri̇m Veri̇mleri̇ni̇n Tahmi̇ni̇ Amaciyla Yapay Si̇ni̇r Ağlarinin Kullanimi

Demır¹

2017

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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ÖZBiyolojik azot giderimi gerçekleştirilen atıksu arıtma proseslerinin (AO prosesi) modellenmesi amacıyla Aktif Çamur Modeli No. 1 (ASM1) kullanılagelmişse de bu modelde ihtiyaç duyulan girdi parametrelerinin tahmin edilmesi çok zaman almaktadır. Bu çalışma kapsamında, ASM1 kadar detaylı girdi verisi gerektirmeyen geri beslemeli yapay sinir ağlarının ( USE OF ARTIFICIAL NEURAL NETWORKS AS A TOOL TO PREDICT CARBON AND NITROGEN REMOVAL EFFICIENCIES IN BIOLOGICAL WASTEWATER TREATMENT PLANTS ABSTRACTAlthough Activated Sludge Model No. 1 (ASM1) was used for modelling biological nitrogen removal processes, estimation of input parameters required to run this model necessitates complicated laboratory analyses. In this study, the performance of Backpropagation Artificial Neural Networks (BPANN), which requires considerably less numbers of input parameters, in predicting chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), and total nitrogen (TN) removal efficiencies was tested. For this purpose, four activation functions were employed in BPANN. Results suggested that COD, TKN, and TN removal efficiencies in AO processes can be accurately estimated using BPANN, with the highest learning and prediction capacity when Sinc function is employed. The mean square errors (MSEs) with Sinc-BPANN were calculated as 2.5010 -4 for COD removal efficiency, 4.1510 -4 for TKN removal efficiency, and 2.6510 -4 for TN removal efficiency. Therefore, the Sinc-BPANN is concluded to be an efficient tool for estimating nonlinear nature of COD, TKN, and TN removal efficiencies in AO processes using considerably less numbers of input parameters.

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Artificial neural network modeling in competitive adsorption of phenol and resorcinol from water environment using some carbonaceous adsorbents

Cited by 152 publications

References 22 publications

Modeling of biosorption of Cu(II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN)

Modeling of biosorption of Cu(II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN)

Methods for the Removal of Phenolic Derivatives from Aqueous Effluents

Bi̇yoloji̇k Atiksu Aritma Tesi̇sleri̇nde Karbon Ve Azot Gi̇deri̇m Veri̇mleri̇ni̇n Tahmi̇ni̇ Amaciyla Yapay Si̇ni̇r Ağlarinin Kullanimi

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