Modeling the removal of Sunfix Red S3B from aqueous solution by electrocoagulation process using artificial neural network

Main Article Content

Ha Manh Bui

Abstract

This study presents an application of artificial neural networks (ANNs) to predict the dye removal efficiency (color and chemical oxygen demand value) of Electrocoagulation process from Sunfix Red S3B aqueous solution. The Bayesian regulation algorithm was applied to train the networks with experimental data including five factors: pH, current density, sulphate concentration, initial dye concentration (IDC), and electrolysis time. The predicting performance of the ANN models was validated through the low root mean square error value (9.844 %), mean absolute percentage error (13.776 %) and the high determination coefficient value (0.836). Garson, Connection weight method and neural interpretation diagram were also used to study the influence of input variables on dye removal efficiency. For decolorization, the most effective inputs are determined as current density, electrolysis time and initial pH, while COD removal is found to be strongly affected by initial dye concentration and sulphate concentration. Through these steps, we demonstrated ANNs robustness in modeling and analysis of electrocoagulation process.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
[1]
H. M. Bui, “Modeling the removal of Sunfix Red S3B from aqueous solution by electrocoagulation process using artificial neural network”, J. Serb. Chem. Soc., vol. 81, no. 8, pp. 959–970, Sep. 2016.
Section
Environmental Chemistry
Author Biography

Ha Manh Bui, Department of Environmental Sciences, Sai Gon university, 273 An Duong Vuong St., District 5, Ho Chi Minh 700000

Department of Environmental Science

References

A. Gottlieb, C. Shaw, A. Smith, A. Wheatley, S. Forsythe, J. Biotechnol. 110 (2003) 49.

S. Sadhasivam, E. Saritha, S. Savitha, K. Swaminathan, Bull. Environ. Contam. Toxicol. 75 (2005) 1046.

T.-H. Kim, C. Park, S. Kim, J. Clean Prod. 13 (2005) 779.

J.-W. Lee, S.-P. Choi, R. Thiruvenkatachari, W.-G. Shim, H. Moon, Water Res. 40 (2006) 435.

L. Zou, B. Zhu, J. Photochem. Photobiol. A 196 (2008) 24.

N. Daneshvar, A. R. Khataee, A. R. Amani Ghadim, M. H. Rasoulifard, J. Hazard. Mater. 148 (2007) 566.

N. Daneshvar, A. Oladegaragoze, N. Djafarzadeh, J. Hazard. Mater. 129 (2006) 116.

M. Y. A. Mollah, P. Morkovsky, J. A. G. Gomes, M. Kesmez, J. Parga, D. L. Cocke, J. Hazard. Mater. 114 (2004) 199.

A. Cerqueira, C. Russo, M. R. C. Marques, Brazilian Journal of Chemical Engineering 26 (2009) 659.

S. Sadri Moghaddam, M. R. Alavi Moghaddam, M. Arami, J. Environ. Manage. 92 (2011) 1284.

M. Khayet, A. Y. Zahrim, N. Hilal, Chem. Eng. J. 167 (2011) 77.

S. Sadri Moghaddam, M. R. Alavi Moghaddam, M. Arami, J. Hazard. Mater. 175 (2010) 651.

B. Lamrini, A. Benhammou, A. Karama, M. V. Lann, Advances in Carbohydrate Chemistry, Springer Vienna, 2005, p. 79.

N. Daneshvar, A. R. Khataee, N. Djafarzadeh, J. Hazard. Mater. 137 (2006) 1788.

L. S. Clesceri, A. E. Greenberg, A. D. Eaton, Standard Methods for the Examination of Water and Wastewater, 20th Edition, APHA American Public Health Association, Section 4500-H+, 1998, p. 79.

E. S. Elmolla, M. Chaudhuri, M. M. Eltoukhy, J. Hazard. Mater. 179 (2010) 127.

F. Masood, M. Ahmad, M. Ansari, A. Malik, Bull. Environ. Contam. Toxicol. 88 (2012) 563.

M. Al-Abri, K. Al Anezi, A. Dakheel, N. Hilal, Desalination 253 (2010) 153.

K. P. Oliveira-Esquerre, M. Mori, R. E. Bruns, Brazilian Journal of Chemical Engineering 19 (2002) 365.

F. Geyikçi, E. Kılıç, S. Çoruh, S. Elevli, Chem. Eng. J. 183 (2012) 53.

M. S. Bhatti, D. Kapoor, R. K. Kalia, A. S. Reddy, A. K. Thukral, Desalination 274 (2011) 74.

Y. Abdollahi, A. Zakaria, M. Abbasiyannejad, H. R. F. Masoumi, M. G. Moghaddam, K. A. Matori, H. Jahangirian, A. Keshavarzi, Chemistry Central Journal 7 (2013) 1.

L. Jing, B. Chen, B. Zhang, Water, Air, & Soil Pollution 225 (2014) 1.

S. Banik, R. Rangayyan, J. E. L. Desautels, Computer-aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer, Morgan & Claypool Publishers, California, United States of America, 2013, p. 79.

D.-J. Choi, H. Park, Water Res. 35 (2001) 3959.

M. Côté, B. P. A. Grandjean, P. Lessard, J. Thibault, Water Res. 29 (1995) 995.