Artificial neural network modeling of photocatalytic removal of a disperse dye using synthesized of ZnO nanoparticles on montmorillonite

Kıranşan, Murat; Khataee, Alireza; Karaca, Semra; Sheydaei, Mohsen

doi:10.1016/j.saa.2014.12.100

Cited by 66 publications

(23 citation statements)

References 24 publications

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“…The optimal architecture of the ANN model was selected by considering the maximum value of R 2 and the lowest value of the MSE of the testing set . Too few neurons cause underfitting, and too many neurons in the hidden layer lead to overfitting the model by raising the complexity of the model . Plotting the MSE versus the number of neurons in the hidden layer confirms that construction of the model with 1–24 of neurons in the hidden layer permits the accuracy and repeatable prediction of experimental data (Fig.…”

Section: Resultsmentioning

confidence: 99%

Photocatalytic Degradation of Oxytetracycline in Aqueous Solutions with TiO₂in Suspension and Prediction by Artificial Neural Networks

Bennemla

Chabani

Amrane

2016

Int. J. Chem. Kinet.

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In this study, the photocatalytic degradation of oxytetracycline (OTC) in aqueous solutions has been studied under different conditions such as initial pollutant concentrations, amount of catalyst, and pH of the solution. Experimental results showed that photocatalysis was clearly the predominant process in the pollutant degradation, since OTC adsorption on the catalyst and photolysis are negligible. The optimal TiO 2 concentration for OTC degradation was found to be 1.0 g/L. The apparent rate constant decreased, and the initial degradation rate increased with increasing initial OTC concentration with the other parameters kept unchanged. Subsequently, data obtained from photocatalytic degradation were used for training the artificial neural networks (ANN). The Levenberg-Marquardt algorithm, log sigmoid function in the hidden layer, and the linear activation function in the output layer were used. The optimized ANN structure was four neurons at the input layer, eighteen neurons at the hidden layer, and one neuron at the output layer. The application of 18 hidden neurons allowed to obtain the best values for R 2 and the mean squared error, 0.99751 and 7.504e-04, respectively, showing the relevance of the training, and hence the network can be used for final prediction of

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Section: Resultsmentioning

confidence: 99%

Photocatalytic Degradation of Oxytetracycline in Aqueous Solutions with TiO₂in Suspension and Prediction by Artificial Neural Networks

Bennemla

Chabani

Amrane

2016

Int. J. Chem. Kinet.

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“…ZnO nanoparticles have a higher photocatalytic capacity under UV-C irradiation compared to UV-A and UV-B conditions. Hence, ZnO nanoparticles-UV-C combination has been utilised widely in photodegradation studies (Kıranş an et al, 2015) and for the disinfection of drinking water (Noroozi et al, 2011). According to previous studies, UV-C irradiation significantly accelerated the dissolution of ZnO (Han et al, 2010) and photochemical ROS generation .…”

Section: Introductionmentioning

confidence: 99%

Cytotoxicity of ZnO NPs towards fresh water algae Scenedesmus obliquus at low exposure concentrations in UV-C, visible and dark conditions

et al. 2015

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“…Building an appropriate ANN model needs many more experiments with respect to RSM. Nevertheless, ANN can also work well even with sensible small data, if the data in the input and output domains are statistically significant . Therefore, the experimental data obtained through RSM are adequate to build an effective ANN model.…”

Section: Resultsmentioning

confidence: 99%

Statistical optimization and modeling approach for azo dye decolorization: Combined effects of ultrasound waves and nanomaterial‐based adsorbent

Pooralhossini

Zanjanchi

Ghaedi

et al. 2018

Applied Organom Chemis

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This study is devoted to an investigation of the effects of sonication time, adsorbent mass, pH and sunset yellow (SY) and disulfine blue (DB) concentration on the removal of DB and SY from water. Artificial neural network and response surface methodology approaches were used to optimize an analytical model to calculate the DB and SY removal performance of tin oxide nanoparticles loaded on activated carbon. The performance of both models was statistically evaluated in terms of the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE) and absolute average deviation (AAD), and graphical plots were also used for comparison of the models. The obtained results show that the artificial neural network model outperforms the classical statistical model in terms of R2, RMSE, MAE and AAD for both dyes. Various isotherm models were studied for fitting the experimental equilibrium data, and the results confirm the applicability of the Langmuir isotherm for description of the adsorption equilibrium. Various kinetic models were applied to the experimental data and the results reveal that the pseudo‐second‐order model with better correlation is superior to the other kinetic models. The significant factors were optimized using the desirability function approach combined with central composite design. The obtained optimal point is located in the valid region and the experimental confirmation indicates good agreement between the predicted optimal points and the experimental data.

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Artificial neural network modeling of photocatalytic removal of a disperse dye using synthesized of ZnO nanoparticles on montmorillonite

Cited by 66 publications

References 24 publications

Photocatalytic Degradation of Oxytetracycline in Aqueous Solutions with TiO₂in Suspension and Prediction by Artificial Neural Networks

Photocatalytic Degradation of Oxytetracycline in Aqueous Solutions with TiO₂in Suspension and Prediction by Artificial Neural Networks

Cytotoxicity of ZnO NPs towards fresh water algae Scenedesmus obliquus at low exposure concentrations in UV-C, visible and dark conditions

Statistical optimization and modeling approach for azo dye decolorization: Combined effects of ultrasound waves and nanomaterial‐based adsorbent

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Artificial neural network modeling of photocatalytic removal of a disperse dye using synthesized of ZnO nanoparticles on montmorillonite

Cited by 66 publications

References 24 publications

Photocatalytic Degradation of Oxytetracycline in Aqueous Solutions with TiO2in Suspension and Prediction by Artificial Neural Networks

Photocatalytic Degradation of Oxytetracycline in Aqueous Solutions with TiO2in Suspension and Prediction by Artificial Neural Networks

Cytotoxicity of ZnO NPs towards fresh water algae Scenedesmus obliquus at low exposure concentrations in UV-C, visible and dark conditions

Statistical optimization and modeling approach for azo dye decolorization: Combined effects of ultrasound waves and nanomaterial‐based adsorbent

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Product

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Photocatalytic Degradation of Oxytetracycline in Aqueous Solutions with TiO₂in Suspension and Prediction by Artificial Neural Networks

Photocatalytic Degradation of Oxytetracycline in Aqueous Solutions with TiO₂in Suspension and Prediction by Artificial Neural Networks