Predicting Nanofiltration Performance during Treatment of Welding Electrode Manufacturing Wastewater, Using Artificial Neural Networks

Abstract:

This paper presents artificial neural network (ANN) predictions for a nanofiltration membrane used to treat wastewater of welding electrode manufacturing in a cross flow set up. The main parameters were time, feed flow rate, and transmembrane pressure (TMP). The experimental data were correlated and analyzed using ANN. ANN’s prediction of the permeate flux, turbidity, total dissolved solids (TDS), hardness for various TMPs, and flow rates are discussed. The effects of the training algorithm, neural network architectures, and transfer function on the ANN performance, as reflected by the percentage average absolute deviation, are discussed. A network with one input layer, 50-100 hidden layers, and one output layer is found to be adequate for mapping input–output relationships and providing a good interpolative tool. A good agreement has been obtained between the ANN predictions and the experimental data with a deviation below 2% for all cases considered.

• Periodical: Applied Mechanics and Materials (Volume 618)
• Pages: 55-59
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.618.55
• Online since: August 2014
• Authors: H. Alizadeh Golestani
• Keywords: Artificial Neural Network (ANN), Industrial Wastewater Treatment, Nanofiltration, Welding Electrode

References

[1] Lau, W. -J. and A.F. Ismail, Polymeric nanofiltration membranes for textile dye wastewater treatment: Preparation, performance evaluation, transport modelling, and fouling control - a review. Desalination, 2009. 245(1-3): pp.321-348.

DOI: 10.1016/j.desal.2007.12.058

[2] Capar, G., U. Yetis, and L. Yilmaz, The most effective pre-treatment to nanofiltration for the recovery of print dyeing wastewaters. Desalination, 2007. 212(1-3): pp.103-113.

DOI: 10.1016/j.desal.2006.09.020

[3] Ahmad, A.L., S. Ismail, and S. Bhatia, Membrane treatment for palm oil mill effluent: Effect of transmembrane pressure and crossflow velocity. Desalination, 2005. 179(1-3): pp.245-255.

DOI: 10.1016/j.desal.2004.11.071

[4] Ahmad, A.L. and B.S. Ooi, A study on acid reclamation and copper recovery using low pressure nanofiltration membrane. Chemical Engineering Journal. In Press, Corrected Proof.

DOI: 10.1016/j.cej.2009.10.014

[5] Aouni, A., et al., Treatment of textile wastewater by a hybrid electrocoagulation/nanofiltration process. Journal of Hazardous Materials, 2009. 168(2-3): pp.868-874.

DOI: 10.1016/j.jhazmat.2009.02.112

[6] Amar, N.B., et al., Comparison of tertiary treatment by nanofiltration and reverse osmosis for water reuse in denim textile industry. Journal of Hazardous Materials, 2009. 170(1): pp.111-117.

DOI: 10.1016/j.jhazmat.2009.04.130

[7] Alcaina-Miranda, M.I., et al., Nanofiltration as a final step towards textile wastewater reclamation. Desalination, 2009. 240(1-3): pp.290-297.

DOI: 10.1016/j.desal.2008.02.028

[8] Amy, G., Fundamental understanding of organic matter fouling of membranes. Desalination, 2008. 231(1-3): pp.44-51.

DOI: 10.1016/j.desal.2007.11.037

[9] Benítez, F.J., et al., The use of ultrafiltration and nanofiltration membranes for the purification of cork processing wastewater. Journal of Hazardous Materials, 2009. 162(2-3): pp.1438-1445.

DOI: 10.1016/j.jhazmat.2008.06.036

[10] Darwish, N.A., et al., Neural Networks Simulation of the Filtration of Sodium Chloride and Magnesium Chloride Solutions Using Nanofiltration Membranes. Chemical Engineering Research and Design, 2007. 85(4): pp.417-430.

DOI: 10.1205/cherd06037

[11] Hong, S. and M. Elimelech, Chemical and physical aspects of natural organic matter (NOM) fouling of nanofiltration membranes. Journal of Membrane Science, 1997. 132(2): pp.159-181.

DOI: 10.1016/s0376-7388(97)00060-4

[12] Comerton, A.M., R.C. Andrews, and D.M. Bagley, The influence of natural organic matter and cations on fouled nanofiltration membrane effective molecular weight cut-off. Journal of Membrane Science, 2009. 327(1-2): pp.155-163.

DOI: 10.1016/j.memsci.2008.11.013

[13] Patterson, D.A., et al., Membrane characterisation by SEM, TEM and ESEM: The implications of dry and wetted microstructure on mass transfer through integrally skinned polyimide nanofiltration membranes. Separation and Purification Technology, 2009. 66(1): pp.90-97.

DOI: 10.1016/j.seppur.2008.11.022

[14] Wang, K.Y. and T. -S. Chung, Fabrication of polybenzimidazole (PBI) nanofiltration hollow fiber membranes for removal of chromate. Journal of Membrane Science, 2006. 281(1-2): pp.307-315.

DOI: 10.1016/j.memsci.2006.03.045

[15] Comerton, A.M., R.C. Andrews, and D.M. Bagley, The influence of natural organic matter and cations on the rejection of endocrine disrupting and pharmaceutically active compounds by nanofiltration. Water Research, 2009. 43(3): pp.613-622.

DOI: 10.1016/j.watres.2008.11.003

[16] Anim-Mensah, A.R., W.B. Krantz, and R. Govind, Studies on polymeric nanofiltration-based water softening and the effect of anion properties on the softening process. European Polymer Journal, 2008. 44(7): pp.2244-2252.

DOI: 10.1016/j.eurpolymj.2008.04.036

[17] Bowen, W.R., et al., Modelling the performance of membrane nanofiltration-application to an industrially relevant separation. Journal of Membrane Science, 2004. 242(1-2): pp.211-220.

DOI: 10.1016/j.memsci.2004.04.028