Modeling the Prediction of Flux and Fouling Parameters of PVDF Nanocomposite Ultrafiltration Membranes with Carbon Nanotubes using Artificial Intelligence Networks

Document Type : Research Paper

Authors

Material and Energy Research Center, Karaj, Iran

Abstract

In this research, the performance, efficiency, and properties of anti-fouling and flux of poly vinylidene fluoride (PVDF) nano-composite membranes with concentration of 15wt.% and 18 wt.%, mixed with different functional  carbon nanotubes (-OH, -COOH, -NH2), were made and studied using phase inversion and normal methylpyrrolidone (NMP) solvent; moreover, the  nano-composite membranes tested for flux, fouling, contact angle, porosity and  protein rejection rate. Also, by using empirical test results, (1) flux and (2) fouling parameters were modeled based on the input variables including nanoparticle percentage, polymer percentage, porosity, contact angle and protein rejection rate. In this model, four intelligent systems including multiple layer percepton, radial basis function, least squares support vector machine and adaptive neuro-fuzzy inference system and  three optimization algorithms including generic algorithm, simulated annealing and particle swarm optimization have been used. The results showed that for both flux and fouling parameters, the best models are GA-RBF and Conjugate-ANFIS with high correlation coefficient. In the next section, modeling was used to obtain optimal values of the best models made for both outputs (minimum fouling and maximum flux) and then the combined algorithm of the genetic algorithm and particle swarm optimization values were obtained. Afterward, by using optimization results for each type of polymer (15wt% and 18wt%), the membranes were made in the laboratory, and then flux, fouling, contact angle and porosity tests were performed, and the results were compared with the results of  the model. Finally, the results showed that 0.07 wt.% single-walled carbon nanotube-PVDF nanocomposite membrane functionalized with hydroxyl group and 0.17 wt.% single-walled carbon nanotube-PVDF nanocomposite membrane functionalized with  hydroxyl group had  the best performance with the polymers of 15 wt.% and 18 wt.% of PVDF respectively.
 

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References

[1]. Delgrange N., Cabassud C., Cabassud M., Durand-Bourlier L. and Lain J. M., “Neural network for prediction of ultrafiltration transmembane pressure—application to drink water,” Journal of  Membr. Sci., Vol. 150, Issue 111, pp. 111-123, 1998. ##
[2]. Razavi S. M. A., Mortazavi S .A. and Mousavi S. M., “Dynamic modeling of milk ultrafiltration by artificial neural network,” Journal of Membr. Sci., Vol. 220, p. 47, 2003. ##
[3]. Razavi S. M. A, Mousavi S. M. and Mortazavi S. A., “Dynamic prediction of milk ultrafiltration performance,A neural network approach,” J. Chem. Eng. Sci., Vol. 58, p. 4185, 2003.##
[4]. Razavi S. M., Mortazavi S. A., Mousavi S. M., “Application of neural networks for crossflow milk ultrafiltration simulation,” Internat. Dairy J., Vol. 14, p. 69, 2004.##
[5]. Teodosiu C., Pastravanu D. and Macoveanu M., “Neural network model for ultrafiltration and backwashing,” Water Research, Vol. 34 , p. 4371, 2000. ##
[6]. Dornier M., Decloux M., Trystram G. and Lebert A., “Dynamic modeling of crossflow microfiltration using neural networks,” Journal of Membr. Sci., Vol. 98 , p. 263, 1999.##
[7]. Mousavi M., Avami A., “Modeling and simulation of water softening by nanofiltration using artificial neural network,” Iran. J. Chem. Eng., Vol. 25, p. 37, 2006.##
[8] Bowen W. R., Jones M. G. and Yousef H. N. S., “Prediction of the rate of crossflow membrane ultrafiltration of col loids: a neural network approach,” J. of Chem. Eng. Sci., Vol. 53, p. 3793, 1998.##
[9]. Salehi v. and Razavi S. M. A., “Modeling of waste brine nanofiltration process using artificial neural network and adaptive neuro- fuzzy inference system,” Desaln. and Water Treat., Vol. 57, p. 14369, 2016.##
[10]. Razavi F. and Razavi S. M. M. A., “Dynamic modeling of flux and total hydraulic resistance in nanofiltration treatment of regeneration waste brine using artificial neural networks,” Desaln. and Water Treat., Vol. 41, No. 3, p. 95, 2012.##
[11]. Vatanpour V., Madaeni S. S., Moradian R., Zinadini S. and Astinchap B., “Fabrication and characterization of novel antifouling nanofiltration membrane prepared from oxidized multiwalled carbon nanotube/polyethersulfone nanocomposite,” J. Membr. Sci., Vol. 375, p. 284, 2011.##
[12]. Vatanpour V., Esmaeili M. and Farahani M. H., “Fouling reduction and retention increment of polyethersulfone nanofiltration membranes embedded by amine-functionalized multi-walled carbon nanotubes,” J. of Membr. Sci., Vol. 466, p. 70, 2014.##
[13]. Zhang X., Lang W. Z., Yan X., Lou Z.-Y. and Chen v., “Influences of the structure parameters of multi-walled carbon nanotubes(MWNTs) on PVDF/PFSA/O-MWNTs hollow fiber ultrafiltration membranes,” J. of Membr. Sci., Vol. 499, p. 179,2016.##
[14]. Madaeni S. S., Zinadini S. and Vatanpour V. , “Convective flow adsorption of nickel ions in PVDF membrane embedded with multi-walled carbon nanotubes and PAA coating,” Separ. and Puri. Tech., Vol. 80, p. 155, 2011.##
[15]. Majeed S., Fierro D., Buhr v., Wind J., Du B., Boschetti-de-Fierro A. and Abetz V., “Multi-walled carbon nanotubes (MWCNTs) mixed polyacrylonitrile (PAN) ultrafiltration membranes,” J. of Membr. Sci., Vol. 403–404, p. 101, 2012.##
Journal of Petroleum Research
Volume 28, 97-6 - Serial Number 103
January and February 2019
Pages 18-35

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