Artificial neural network modeling of p-cresol photodegradation

Chemistry Central Journal

Ashkan Keshavarzi²  Hossein Jahangirian⁵  Khamirul Amin Matori¹  Mansour Ghaffari Moghaddam⁶  Hamid Reza Fard Masoumi³  Mina Abbasiyannejad⁴  Azmi Zakaria¹  Yadollah Abdollahi¹ 

[1]Material Synthesis and Characterization Laboratory, Institute of Advanced Technology, Universiti Putra Malaysia, Serdang, Selangor 43400 UPM, Malaysia

[2]Otto-Schott-Institut, Jena University, Fraunhoferstr. 6, Jena, 07743, Germany

[3]Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, Serdang, Selangor 43400 UPM, Malaysia

[4]English Department, Faculty of Modern Languages and Communication, Universiti Putra Malaysia, Serdang, Selangor 43400 UPM, Malaysia

[5]Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Selangor, 43400 UPM, Malaysia

[6]Department of Chemistry, Faculty of Science, University of Zabol, Zabol, Iran

关键词: Photocatalyst;    UV-irradiation;    ZnO;    p-cresol;    ANN-modeling;    Photodegradation;   

Others  :  787897
DOI  :  10.1186/1752-153X-7-96

 received in 2013-03-17, accepted in 2013-05-28,  发布年份 2013

【 摘 要 】

Background

The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling p-cresol photodegradation. To optimize the network, the independent variables including irradiation time, pH, photocatalyst amount and concentration of p-cresol were used as the input parameters, while the photodegradation% was selected as output. The photodegradation% was obtained from the performance of the experimental design of the variables under UV irradiation. The network was trained by Quick propagation (QP) and the other three algorithms as a model. To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized. After minimizing the error, the topologies of the algorithms were compared by coefficient of determination and absolute average deviation.

Results

The comparison indicated that the Quick propagation algorithm had minimum root mean squared error, 1.3995, absolute average deviation, 3.0478, and maximum coefficient of determination, 0.9752, for the testing data set. The validation test results of the artificial neural network based on QP indicated that the root mean squared error was 4.11, absolute average deviation was 8.071 and the maximum coefficient of determination was 0.97.

Conclusion

Artificial neural network based on Quick propagation algorithm with topology 4-10-1 gave the best performance in this study.

【 授权许可】

   
2013 Abdollahi et al.; licensee Chemistry Central Ltd.

【 图 表 】

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