Development of Artificial Neural Network for Predicting The Photodegradation of Reactive Black 5 Dye

Dika Rahayu Widiana, Ryan Yudha Adhitya

Abstract


We applied a multilayer artificial neural network (ANN) developed using a Lavenberg–Marquadt algorithm to predict the photodegradation activity of the Reactive Black 5 (RB5) dye. A copper-doped titanium dioxide was employed as a photocatalyst. A copper doped titanium dioxide was synthesized through a wet-impregnation method. To optimize the network the operational parameters including the RB5 initial concentration, photocatalyst dose, irradiation time, hydrogen peroxide concentration, and visible light intensity were used as the input parameter. Removal efficiency of RB5 was selected as output. The number of neurons in the second hidden layer was optimized to determine the suitable ANN model structure for the RB5 removal. ANN based through Levenberg-Marquadth algorithm with structure 1-10-21-1 gave the best performance in this study. The criteria for the applicability of the model were the root mean square error (0.1) and coefficient of correlation (0.98275).


Keywords


Artificial neural network; Levenberg–Marquadt; Copper-doped TiO2; photodegradation; Reactive Black 5

Full Text:

PDF

References


Kant, R. (2012). Textile dyeing industry an environmental hazard. Natural Science, 4, 22–26.

Lucas, M.S., Peres, J.A. (2006). Decolorization of the azo dye reactive black 5 by fenton and photo-fenton oxidation. Dyes and Pigments, 71, 236–244.

Kuyumcu, O.K., Kibar, E., Dayioglu, K., Gedik, F., Akin, A.N., Aydinoglu, S.O. (2015). A comparative study for removal of different dyes over M/TiO2 (M = Cu, Ni, Co, Fe, Mn and Cr) photocatalysts under visible light irradiation. Journal of Photochemistry and Photobiology A: Chemistry, 311, 176–185.

Nakata, K., Fujishima, A. (2012). Invited review TiO2 photocatalysis: Design and applications. Journal of Photochemistry and Photobiology C: Photochemistry Reviews, 13, 169–189.

Tian, F., Wu, Z., Yan, Y., Ye, B.C., Liu, D. (2016). Synthesis of visible-light-responsive Cu and N-codoped AC/TiO2 photocatalyst through microwave irradiation. Nanoscale Research Letters, 11, 292.

Eskandarloo, H., Badiei, A., Behnajady, M.A., Ziarani, G.M. (2016). Hybrid homogeneous and heterogeneous photocatalytic processes for removal of triphenylmethane dyes: artificial neural network modeling. Clean Soil Air Water, 44, 739–739.

Abdollahi, Y., Zakaria, A., Abbasiyannejad, M., Masoumi, H.R.F., Moghaddam, M.G., Matori, K.A., Jahangirian, H., Keshavarz, A. (2013). Artificial neural network modeling of p-cresol photodegradation. Chemistry Central Journal, 7, 96.

Abdollahi, Y., Zakaria, A., Sairi, N.A., Matori, K.A., Masoumi, H.R.F., Sadrolhosseini, A.A., Jahangirian, H. (2014). Artificial neural network modelling of photodegradation in suspension of manganese doped zinc oxide nanoparticles under visible-light irradiation. The Scientific World Journal, 2014, 1.

Pakrou, V., Pakrou, S., Nasermehrdadi, Amiri, M.J. (2015). Prediction of pollutant removal in the treatment plant of industrial shahid salimi town using ANN. Current World Environment, 10, 899–907.

Li, C., Sun, Z., Xue, Y., Yao, G., Zheng, S. (2016). A facile synthesis of g-C3N4/TiO2 hybrid photocatalyst by sol-gel method and its enhanced photodegradation towards methylene blue under visible light. Advanced Power Technology, 27, 330-337.

Boningari, T., Pappas, D.K., Ettireddy, P.R., Kotrba, A., Smirniotis, P.G. (2015). Influence of SiO2 on M/TiO2 (M = Cu, Mn, and Ce) formulations for low-temperature selective catalytic reduction of NOx with NH3: surface properties and key components in relation to the activity of NOx reduction. Industrial & Engineering Chemistry Research, 54, 2261–2273.

Tu, J.V. (1996). Advantages and disadvantages of using Artificial Neural Networks versus Logistic Regression for predicting medical outcomes. J Clin Epidemiol, 49, 1225–1231.

Syai’in, M., Soeprijanto, A., Yuniarno, E.M. (2011). New algorithm for neural network optimal power flow (NN-OPF) including generator capability curve constraint and statistic-fuzzy load clustering. International Journal of Computer Applications, 36, 1.

David, A.O., Tahir, M., Amin, N.A.S. (2015). Copper modified TiO2 and g-C3N4 catalysts for photoreduction of CO2 to methanol using different reaction mediums. Malaysian Journal of Fundamental and Applied Sciences, 11, 102–105.

Wang, W.K., Chen, J.J., Zhang, X., Huang, Y.X., Li, W.W., Yu, H.Q. (2016). Self-induced synthesis of phase-junction TiO2 with a tailored rutile to anatase ratio below phase transition temperature. Scientific, 6, 1.

Sohrabi, S., Akhlaghian, F. (2016). Modeling and optimizaton of phenol degradation over copper-doped titanium dioxide photocatalyst using response surface methodolgy. Process Safety and Environmental Protection, 99, 120–128.

Das, L., Maity, U., Basu, J.K. (2014). The photocatalytic degradation of carbamazepine and prediction by artificial neural networks. Process Safety and Environmental Protection, 92, 888–895.

Tseng, D.H., Juang, L.C., Huang, H.H. (2012). Effect of oxygen and hydrogen peroxide on the photocatalytic degradation of monochlorobenzene in TiO2 aqueous suspension. International Journal of Photoenergy, 2012, 1.

Saggioro, E.M., Oliveira, A.S., Pavesi, T., Maia, C.G., Ferreira, .F.V., Moreira, J.C. (2011). Use of titanium dioxide photocatalysis on the remediation of model textile wastewaters containing azo dyes. Molecules, 16, 10370–10386.

Rasoulifard, M.H., Dorraji, M.S.S., Ghadim, A.R.A., Babaeinezhad, N.K. (2016). Visible-light photocatalytic activity of chitosan/polyaniline/CdS nanocomposite: Kinetic studies and artificial neural network modeling. Applied Catalysis A: General, 514, 60–70.

Jayalakshmi, T., Santhakumaran, A. (2011). Statistical normalization and back propagation for classification. International Journal of Computer Theory and Engineering, 3, 1793–8201.




DOI: https://doi.org/10.31284/j.iptek.2019.v23i2.547

Refbacks

  • There are currently no refbacks.


 

Indexed by:

Sinta S3Google Scholar GARUDA Garba Rujukan DigitalDimensions Logo