Modeling of waste brine nanofiltration process using artificial neural network and adaptive neuro-fuzzy inference system

References

• F. Salehi, S.M.A. Razavi, M. Elahi, Purifying anion exchange resin regeneration effluent using polyamide nanofiltration membrane, Desalination 278 (2011) 31–35.10.1016/j.desal.2011.04.067
   Web of Science ®Google Scholar
• S. Cartier, M.A. Theoleyre, M. Decloux, Treatment of sugar decolorizing resin regeneration waste using nanofiltration, Desalination 113 (1997) 7–17.10.1016/S0011-9164(97)00110-0
   Web of Science ®Google Scholar
• S. Wadley, C.J. Brouckaert, L.A.D. Baddock, C.A. Buckley, Modelling of nanofiltration applied to the recovery of salt from waste brine at a sugar decolourisation plant, J. Membr. Sci. 102 (1995) 163–175.10.1016/0376-7388(94)00284-6
   Web of Science ®Google Scholar
• N.A. Darwish, N. Hilal, H. Al-Zoubi, A.W. Mohammad, Neural networks simulation of the filtration of sodium chloride and magnesium chloride solutions using nanofiltration membranes, Chem. Eng. Res. Des. 85 (2007) 417–430.
   Web of Science ®Google Scholar
• H. Yacubowicz, J. Yacubowicz, Nanofiltration: Properties and uses, Filtr. Sep. 42 (2005) 16.10.1016/S0015-1882(05)70617-0
   Web of Science ®Google Scholar
• L.P. Raman, M. Cheryan, N. Rajagopalan, Consider nanofiltration for membrane separation, Chem. Eng. Prog. 90 (1994) 68–74.
   Web of Science ®Google Scholar
• K.Y. Wang, T.S. Chung, The characterization of flat composite nanofiltration membranes and their applications in the separation of Cephalexin, J. Membr. Sci. 247 (2005) 37.10.1016/j.memsci.2004.09.007
   Web of Science ®Google Scholar
• W.R. Richard Bowen, M.G. Jones, J.S. Welfoot, H.N.S. Yousef, Predicting salt rejections at nanofiltration membranes using artificial neural networks, Desalination 129 (2000) 147–162.10.1016/S0011-9164(00)00057-6
   Web of Science ®Google Scholar
• M.A. Mashrei, N. Abdulrazzaq, T.Y. Abdalla, M.S. Rahman, Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members, Eng. Struct. 32 (2010) 1723–1734.10.1016/j.engstruct.2010.02.024
   Web of Science ®Google Scholar
• M. Bahramparvar, F. Salehi, S.M.A. Razavi, Predicting total acceptance of ice cream using artificial neural network, J. Food Process. Preserv. 38(3) (2014) 1080–1088.10.1111/jfpp.12066
   Web of Science ®Google Scholar
• H. Demuth, M. Beale, User’s guide for Neural Network Toolbox for Use with Matlab, The Math Works Inc. 2000, pp. 1–7.
   Google Scholar
• F. Salehi, S.M.A. Razavi, Dynamic modeling of flux and total hydraulic resistance in nanofiltration treatment of regeneration waste brine using artificial neural networks, Desalin. Water Treat. 41 (2012) 95–104.10.1080/19443994.2012.664683
   Web of Science ®Google Scholar
• G.R. Shetty, H. Malki, S. Chellam, Predicting contaminant removal during municipal drinking water nanofiltration using artificial neural networks, J. Membr. Sci. 212 (2003) 99–112.10.1016/S0376-7388(02)00473-8
   Web of Science ®Google Scholar
• C. Teodosiu, D. Pastravanu, M. Macoveanu, Neural network models for ultrafiltration and backwashing, Water Res 34 (2000) 4371–4380.10.1016/S0043-1354(00)00217-7
   Web of Science ®Google Scholar
• V. Yangali-Quintanilla, A. Verliefde, T.U. Kim, A. Sadmani, M. Kennedy, G. Amy, Artificial neural network models based on QSAR for predicting rejection of neutral organic compounds by polyamide nanofiltration and reverse osmosis membranes, J. Membr. Sci. 342 (2009) 251–262.10.1016/j.memsci.2009.06.048
   Web of Science ®Google Scholar
• E. Entchev, L. Yang, Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation, J. Power Sources 170 (2007) 122–129.10.1016/j.jpowsour.2007.04.015
   Web of Science ®Google Scholar
• M. Shieh, K. Chang, C. Chuang, J. Chiou, J. Li, ANFIS based controller design for biped robots, in: Proceedings of International Conference on Mechatronics, Kumamoto, Japan, (2007) 1–6.
   Google Scholar
• M. Zengqiang, P. Cunzhi, W. Yongqiang, Road safety evaluation from traffic information based on ANFIS, in: Proceedings of the 27th Chinese Control Conference, Kunming, China. (2008) 554–558.
   Google Scholar
• N. Saghatoleslami, M. Mousavi, J. Sargolzaei, A neuro-fuzzy model for a dynamic prediction of milk ultrafiltration flux and resistance, Iran. J. Chem. Chem. Eng. 26 (2007) 53–61.
   Web of Science ®Google Scholar
• J. Sargolzaei, M. Khoshnoodi, N. Saghatoleslami, M. Mousavi, Fuzzy inference system to modeling of crossflow milk ultrafiltration, Appl. Soft Comput. 8 (2008) 456–465.10.1016/j.asoc.2007.02.007
   Web of Science ®Google Scholar
• N. Hilal, O.O. Ogunbiyi, M. Al-Abri, Neural network modeling for separation of bentonite in tubular ceramic membranes, Desalination 228 (2008) 175–182.10.1016/j.desal.2007.10.006
   Web of Science ®Google Scholar
• N. Delgrange, C. Cabassud, M. Cabassud, L. Durand-Bourlier, J.M. Lainé, Neural networks for prediction of ultrafiltration transmembrane pressure—application to drinking water production, J. Membr. Sci 150 (1998) 111–123.10.1016/S0376-7388(98)00217-8
   Web of Science ®Google Scholar
• S.S. Tambe, B.D. Kulkami, P.B. Deshpande, Elements of Artificial Neural Networks with Selected Applications in Chemical Engineering, and Chemical and Biological Sciences, Simulation and Advanced Controls Ltd, Louisville, KY, USA, 1996.
   Google Scholar
• S. Chellam, Artificial neural network model for transient crossflow microfiltration of polydispersed suspensions, J. Membr. Sci. 258 (2005) 35–42.10.1016/j.memsci.2004.11.038
   Web of Science ®Google Scholar
• M.T. Hagan, M. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neural Networks 5 (1994) 989–993.10.1109/72.329697
   PubMed Web of Science ®Google Scholar
• A. Abbas, N. Al-Bastaki, Modeling of an RO water desalination unit using neural networks, J. Chem. Eng. 114 (2005) 139–143.10.1016/j.cej.2005.07.016
   Web of Science ®Google Scholar
• K. Movagharnejad, M. Nikzad, Modeling of tomato drying using artificial neural network, Comput. Electron. Agric. 59 (2007) 78–85.10.1016/j.compag.2007.05.003
   Web of Science ®Google Scholar
• M. Abu Ghoush, M. Samhouri, M. Al Holy, T. Herald, Formulation and fuzzy modeling of emulsion stability and viscosity of a gum-protein emulsifier in a model mayonnaise system, J. Food Eng. 84 (2008) 348–357.
   Web of Science ®Google Scholar
• N. Pramanik, R.K. Panda, Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction, Hydrol. Sci. J. 54(2) (2009) 247–260.10.1623/hysj.54.2.247
   Web of Science ®Google Scholar
• S. Becker, V. Karri, Predictive models for PEM-electrolyzer performance using adaptive neuro-fuzzy inference systems, Int. J. Hydrogen Energy 35 (2010) 9963–9972.10.1016/j.ijhydene.2009.11.060
   Web of Science ®Google Scholar
• T.Y. Pai, T.J. Wan, S.T. Hsu, T.C. Chang, Y.P. Tsai, C.Y. Lin, H.C. Su, L.F. Yu, Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent, Comput. Chem. Eng. 33 (2009) 1272–1278.10.1016/j.compchemeng.2009.02.004
   Web of Science ®Google Scholar
• A. Gulbag, F. Temurtas, A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems, Sens. Actuators, B 115 (2006) 252–262.10.1016/j.snb.2005.09.009
   Web of Science ®Google Scholar
• V. Gökmen, Özge Çetinkaya Açar, A. Serpen, İ. Süğüt, Modeling dead-end ultrafiltration of apple juice using artificial neural network, J. Food Process Eng. 32(2) (2009) 248–264.10.1111/jfpe.2009.32.issue-2
   Web of Science ®Google Scholar