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Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation
Joint Authors
Sadrolhosseini, Amir Reza
Zakaria, Azmi
Amin Matori, Khamirul
Abdollahi, Yadollah
Sairi, Nor Asrina
Fard Masoumi, Hamid Reza
Jahangirian, Hossein
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-11-04
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
The artificial neural network (ANN) modeling of m-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency.
The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation.
The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration of m-cresol while the efficiency was the only response as output.
The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software’s option.
To obtain the optimum topologies, ANN was trained by quick propagation (QP), Incremental Back Propagation (IBP), Batch Back Propagation (BBP), and Levenberg-Marquardt (LM) algorithms for testing data set.
The topologies were determined by the indicator of minimized root mean squared error (RMSE) for each algorithm.
According to the indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies.
Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared.
Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process.
The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots.
The optimum points of the variables were confirmed by further validated experiments.
Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.
American Psychological Association (APA)
Abdollahi, Yadollah& Zakaria, Azmi& Sairi, Nor Asrina& Amin Matori, Khamirul& Fard Masoumi, Hamid Reza& Sadrolhosseini, Amir Reza…[et al.]. 2014. Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1050794
Modern Language Association (MLA)
Abdollahi, Yadollah…[et al.]. Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation. The Scientific World Journal No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1050794
American Medical Association (AMA)
Abdollahi, Yadollah& Zakaria, Azmi& Sairi, Nor Asrina& Amin Matori, Khamirul& Fard Masoumi, Hamid Reza& Sadrolhosseini, Amir Reza…[et al.]. Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1050794
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050794