Membrane and Water Treatment

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Application of ANN modeling for oily wastewater treatment by hybrid PAC-MF process

  • Abbasi, Mohsen (Department of Chemical Engineering, School of Chemical and Petroleum Engineering, Persian Gulf University) ;
  • Rasouli, Yaser (Department of Chemical Engineering, School of Chemical and Petroleum Engineering, Persian Gulf University) ;
  • Jowkar, Peyman (Department of Chemical Engineering, School of Chemical and Petroleum Engineering, Persian Gulf University)
  • Received : 2017.07.25
  • Accepted : 2017.08.19
  • Published : 2018.07.25
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Abstract

In the following study, Artificial Neural Network (ANN) is used for prediction of permeate flux decline during oily wastewater treatment by hybrid powdered activated carbon-microfiltration (PAC-MF) process using mullite and mullite-alumina ceramic membranes. Permeate flux is predicted as a function of time and PAC concentration. To optimize the networks performance, different transfer functions and different initial weights and biases have been tested. Totally, more than 850,000 different networks are tested for both membranes. The results showed that 10:6 and 9:20 neural networks work best for mullite and mullite-alumina ceramic membranes in PAC-MF process, respectively. These networks provide low mean squared error and high linearity between target and predicted data (high $R^2$ value). Finally, the results present that ANN provide best results ($R^2$ value equal to 0.99999) for prediction of permeation flux decline during oily wastewater treatment in PAC-MF process by ceramic membranes.

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References

  1. Abadi, S.R.H., Sebzari, M.R., Hemati, M., Rekabdar, F. and Mohammadi, T. (2011), "Ceramic membrane performance in microfiltration of oily wastewater", Desalination, 265(1), 222-228. https://doi.org/10.1016/j.desal.2010.07.055
  2. Abbasi, M., Mirfendereski, M., Nikbakht, M., Golshenas, M. and Mohammadi, T. (2010), "Performance study of mullite and mullite-alumina ceramic MF membranes for oily wastewaters treatment", Desalination, 259(1), 169-178. https://doi.org/10.1016/j.desal.2010.04.013
  3. Abbasi, M., Reza Sebzari, M. and T. Mohammadi (2011), "Enhancement of oily wastewater treatment by ceramic microfiltration membranes using powder activated carbon", Chem. Eng. Technol., 34(8), 1252-1258. https://doi.org/10.1002/ceat.201100108
  4. Amaral, M.C.S., Andrade, L.H., Neta, L.S.F., Magalhaes, N.C., Santos, F.S., Mota, G.E. and Carvalho, R.B. (2016), "Microfiltration of vinasse: Sustainable strategy to improve its nutritive potential", Water Sci. Tech., 73(6), 1434-1441. https://doi.org/10.2166/wst.2015.606
  5. Baughman, D.R. and Liu, Y.A. (2014), Neural Networks in Bioprocessing and Chemical Engineering. Academic Press, Massachusetts, U.S.A.
  6. Cao, W., Liu, Q., Wang, Y. and Mujtaba, I. M. (2016), "Modeling and simulation of VMD desalination process by ANN", Comput. Chem. Eng., 84, 96-103. https://doi.org/10.1016/j.compchemeng.2015.08.019
  7. Chang, Y.T., Lin, J., Shieh, J.S. and Abbod, M.F. (2012), "Optimization the initial weights of artificial neural networks via genetic algorithm applied to hip bone fracture prediction", Adv. Fuzzy Syst., 2012, 6.
  8. Chattoraj, S., Mondal, N.K., Das, B., Roy, P. and Sadhukhan, B. (2014), "Carbaryl removal from aqueous solution by Lemna major biomass using response surface methodology and artificial neural network", J. Environ. Chem. Eng., 2(4), 1920-1928. https://doi.org/10.1016/j.jece.2014.08.011
  9. Chen, X., Zhang, W., Lin, Y., Cai, Y., Qiu, M. and Fan, Y. (2015), "Preparation of high-flux ${\gamma}$-alumina nanofiltration membranes by using a modified sol-gel method", Microporous Mesoporous Mater., 214, 195-203. https://doi.org/10.1016/j.micromeso.2015.04.027
  10. da Conceicao, V.M., Ugri, A., Bonicontro, M.C., Silveira, C., Nishi, L., Vieira, M.F., de Jesus, B., Vieira, A.M.S. and Bergamasco, R. (2015), "Removal of excess fluoride from groundwater using natural coagulant Moringa oleifera Lam and microfiltration", Can. J. Chem. Eng., 93(1), 37-45. https://doi.org/10.1002/cjce.22101
  11. Djahida, Z., Amel, B., Mourad, T.A., Hayet, D. and Rachida, M. (2014), "Treatment of a dye solophenyle 4GE by coupling electrocoagulation/nanofiltration", Membr. Water Treat., 5(4), 251-263. https://doi.org/10.12989/mwt.2014.5.4.251
  12. Erb, R.J. (1993), "Introduction to backpropagation neural network computation", Pharmaceut. Res., 10(2), 165-170. https://doi.org/10.1023/A:1018966222807
  13. Ganesan, P., Lakshmi, J., Sozhan, G. and Vasudevan, S. (2013), "Removal of manganese from water by electrocoagulation: Adsorption, kinetics and thermodynamic studies", Can. J. Chem. Eng., 91(3), 448-458. https://doi.org/10.1002/cjce.21709
  14. Ghouil, B., Harabi, A., Bouzerara, F., Boudaira, B., Guechi, A., Demir, M.M. and Figoli, A. (2015), "Development and characterization of tubular composite ceramic membranes using natural alumino-silicates for microfiltration applications", Mater. Charact., 103, 18-27. https://doi.org/10.1016/j.matchar.2015.03.009
  15. He, X., Chai, Z., Li, F., Zhang, C., Li, D., Li, J. and Hu, J. (2013), "Advanced treatment of biologically pretreated coking wastewater by electrochemical oxidation using Ti/RuO2-IrO2 electrodes", J. Chem. Technol. Biot., 88(8), 1568-1575. https://doi.org/10.1002/jctb.4006
  16. Hecht-Nielsen, R. (1989), "Theory of the backpropagation neural network", Proceedings of the International Joint Conference on Neural Networks 1, Washington, D.C., U.S.A., June.
  17. Kasim, N., Mohammad, A.W. and Abdullah, S.R.S. (2016), "Performance of membrane filtration in the removal of iron and manganese from Malaysia's groundwater", Membr. Water Treat., 7(4), 277-296. https://doi.org/10.12989/mwt.2016.7.4.277
  18. Lester, J., Jefferson, B., Eusebi, A.L., McAdam, E. and Cartmell, E. (2013), "Anaerobic treatment of fortified municipal wastewater in temperate climates", J. Chem. Technol. Biot., 88(7), 1280-1288. https://doi.org/10.1002/jctb.3972
  19. MacKay, D.J. (1996), "Bayesian methods for backpropagation networks", Models of Neural Networks III, Springer, New York, U.S.A.
  20. Maddah, H.A. and Choglem A.M. (2015), "Applicability of low pressure membranes for wastewater treatment with cost study analyses", Membr. Water Treat., 6(6), 477-488. https://doi.org/10.12989/mwt.2015.6.6.477
  21. Nourouzi, M.M., Chuah. T.G. and Choong, T.S. (2011), "Optimisation of reactive dye removal by sequential electrocoagulation-flocculation method: Comparing ANN and RSM prediction", Water Sci. Technol., 63(5), 984-994. https://doi.org/10.2166/wst.2011.280
  22. Patil, D.S., Chavan, S.M. and Oubagaranadin, J.U.K. (2016), "A review of technologies for manganese removal from wastewaters", J. Environ. Chem. Eng., 4(1), 468-487. https://doi.org/10.1016/j.jece.2015.11.028
  23. Porrazzo, R., Cipollina, A., Galluzzo, M. and Micale, G. (2013), "A neural network-based optimizing control system for a seawater-desalination solar-powered membrane distillation unit", Comput. Chem. Eng., 54, 79-96. https://doi.org/10.1016/j.compchemeng.2013.03.015
  24. Racz, G., Kerker, S., Schmitz, O., Schnabel, B., Kovacs, Z., Vatai, G., Ebrahimi, M. and Czermak, P. (2015), "Experimental determination of liquid entry pressure (LEP) in vacuum membrane distillation for oily wastewaters", Membr. Water Treat., 6(3), 237-249. https://doi.org/10.12989/mwt.2015.6.3.237
  25. Ratanatamskul, C., Glingeysorn, N. and Yamamoto, K. (2012), "The BNR-MBR (Biological Nutrient Removal-Membrane Bioreactor) for nutrient removal from high-rise building in hot climate region", Membr. Water Treat., 3(2), 133-140. https://doi.org/10.12989/mwt.2012.3.2.133
  26. Razavi, S.M.R., Miri, T., Barati, A., Nazemian, M. and Sepasi, M. (2015), "Industrial wastewater treatment by using of membrane", Membr. Water Treat., 6(6), 489-499. https://doi.org/10.12989/mwt.2015.6.6.489
  27. Shokrkar, H., Salahi, A., Kasiri, N. and Mohammadi, T. (2011), "Mullite ceramic membranes for industrial oily wastewater treatment: Experimental and neural network modeling", Water Sci. Technol., 64(3), 670-676. https://doi.org/10.2166/wst.2011.655
  28. Suykens, J.A., Vandewalle, J.P. and de Moor, B.L. (2012), Artificial Neural Networks for Modelling and Control of Nonlinear systems, Springer Science and Business Media, New York, U.S.A.
  29. Werner, J., Besser, B., Brandes, C., Kroll, S. and Rezwan, K. (2014), "Production of ceramic membranes with different pore sizes for virus retention", J. Water Process Eng., 4, 201-211. https://doi.org/10.1016/j.jwpe.2014.10.007
  30. Zhu, H.Y. and Zhou, M. (2014), "Air flotation method in the treatment of oily wastewater application of its Pprogress", Adv. Mater. Res., 971-973, 2044-2047. https://doi.org/10.4028/www.scientific.net/AMR.971-973.2044