Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G on Chara contraria

Çelekli, Abuzer; Geyik, Faruk

doi:10.1016/j.biortech.2011.02.052

Cited by 67 publications

(27 citation statements)

References 20 publications

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“…Biosorption refers to the ability of certain biomaterials to bind and concentrate on toxic pollutants from even the most dilute aqueous solutions [1]. In the case of dyes removal, many biosorbents have been reported in the literature, such as chitosan [18], fungi [19,20], algae [17,21], and bacteria [4,22,23].…”

Section: Introductionmentioning

confidence: 99%

Valorization of an agricultural waste,Stipa tenassicimafibers, by biosorption of an anionic azo dye, Congo red

Chebli

Bouguettoucha

Mekhalef

et al. 2014

Desalination and Water Treatment

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A B S T R A C TThe removal of Congo red dye (CR) from aqueous solutions using a novel low-cost biological adsorbent, Stipa tenassicima fibers, has been investigated in this paper. Batch experiments were conducted to examine the effect of the main parameters, such as the initial CR concentration, the pH, and the temperature on the sorption of the dye. Maximum adsorption removal was observed at pH 4 and biosorption capacity of S. tenassicima was enhanced by increasing the temperature. Rate constants of pseudo-first order, pseudo-second order, and intraparticle diffusion coefficient were calculated to analyze the dynamic of the sorption process; they showed that sorption kinetics followed an intraparticle diffusion model, while the two straight lines describing experimental data indicated that intraparticle diffusion was the limiting step for biosorption. Among the tested isotherm models, the Sips isotherm was found to be the most relevant to describe CR sorption onto S. tenassicima fibers. Thermodynamic parameters, such as changes in standard free energy, enthalpy, and entropy, were also evaluated and the results suggested that the sorption reaction was spontaneous and endothermic in nature. The potential of S. tenassicima fibers, an easily available and low-cost material, to be used as an alternative biosorbent material for the removal of a dye, CR, from aqueous solutions was therefore confirmed.

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Section: Introductionmentioning

confidence: 99%

Valorization of an agricultural waste,Stipa tenassicimafibers, by biosorption of an anionic azo dye, Congo red

Chebli

Bouguettoucha

Mekhalef

et al. 2014

Desalination and Water Treatment

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show abstract

“…4. The vibration peaks at 2,991, 2,884, and 2,784 cm −1 are assigned to asymmetric and symmetric stretching of CH 2 groups [22]. The bands at 1,567 and 1,416 cm −1 belong to ring stretching vibrations [23].…”

Section: Characterization Of Adsorbentsmentioning

confidence: 98%

“…In this study, Neural Network Toolbox V 7.12 of MATLAB mathematical software was used to predict the adsorption efficiency. This multilayered network is a structure consisting of (i) input layer of neuron (independent variables), (ii) a number of hidden layers, and (iii) output layer (dependent variables) [21,22]. The experimental data were separated into input matrix and output matrix.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Development, application, and evaluation of artificial neural network in investigating the removal efficiency of Acid Red 57 by synthesized mesoporous carbon-coated monoliths

Malekbala

Hosseini

Soltani

et al. 2014

Desalination and Water Treatment

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2014): Development, application, and evaluation of artificial neural network in investigating the removal efficiency of Acid Red 57 by synthesized mesoporous carbon-coated monoliths, Desalination and Water TreatmentTo link to this article: http://dx.Acid Red 57 (AR57) has been successfully removed from aqueous solution by adsorption on our synthesized mesoporous carbon-coated monolith (MCCM). For the first time, a powerful artificial neural network (ANN) model has been developed to predict the removal efficiency of AR57 on MCCM. Three critical parameters in adsorption systems, that is, solution's initial pH, initial dye concentration, and contact time were incorporated in the ANN model in order to optimize the observed adsorption process. Langmuir and Freundlich adsorption models were then fitted to the adsorption data to estimate the adsorption capacity. It was concluded that Langmuir isotherm was best-fitted to the data showing a maximum monolayer adsorption capacity of 1,162.7 mg/g. The pseudo-first-order and pseudo-second-order kinetic models were subsequently tested to evaluate the kinetics of the adsorption process. It was revealed that the adsorption kinetics could be better represented by the pseudo-second-order model. A comparison was finally drawn between ANN and pseudo-second-order kinetic models. Based on the error analyses and determination of coefficients, ANN was the more appropriate model to describe the studied adsorption process.

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“…They mentioned that ANN is suitable method to describe the adsorption process. In the scientific literature, there are a lot of papers regarding to ANN modeling on optimization of adsorption process (Myhara, Sablani, Al-Alawi, & Taylor, 1998;Aber, Daneshvar, Soroureddin, Chabok, & AsadpourZeynali, 2007;Yetilmezsoy & Demirel, 2008;Kumar & Porkodi, 2009;Çelekli & Geyik, 2011).…”

Section: Ann Modeling Of Adsorption By Ptpomentioning

confidence: 99%

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Donut¹,

Çavaş²

2017

Turk. J. Fish. Aquat. Sci.

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Importance of the artificial intelligence in the chemical processes has been increased in the recent studies. Although biosorption is widely studied topic in chemistry, modelling of biosorption data is based on very old equations. However, use of artificial intelligence in the biosorption based studies can give important clues to researchers. For this purpose, the biosorption of tetracycline by using Posidonia oceanica from the Mediterranean Sea was studied in this study. According to classical evaluation, the data were well in line with pseudo-second order kinetic and Langmuir's isotherm. In the artificial neural network modelling, the best back propagation algorithm, optimum number of hidden neuron and optimum training:validation:testing ratio were found as Bayesian Regulation, 16 and 70:10:20, respectively. In conclusion, P. oceanica based marine waste can be used in the development of high performance biosorbents for environmental pollutants. However, it should not be forgotten that P.oceanica is a threatened species; therefore, only dead leaves accumulated in recreational area should be collected and evaluated based on the permissions of governmental authorities. The results also exhibited that artificial neural network can also be used in the modelling of the biosorption data in which it helps scientists to estimate biosorption ratio correctly under various conditions.

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Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G on Chara contraria

Cited by 67 publications

References 20 publications

Valorization of an agricultural waste,Stipa tenassicimafibers, by biosorption of an anionic azo dye, Congo red

Valorization of an agricultural waste,Stipa tenassicimafibers, by biosorption of an anionic azo dye, Congo red

Development, application, and evaluation of artificial neural network in investigating the removal efficiency of Acid Red 57 by synthesized mesoporous carbon-coated monoliths

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