|تعداد مشاهده مقاله||111,649,700|
|تعداد دریافت فایل اصل مقاله||86,267,884|
Prediction of RO Membrane Performances by Use of Adaptive Network-Based Fuzzy Interference Systems
|Journal of Chemical and Petroleum Engineering|
|مقاله 9، دوره 54، شماره 1، شهریور 2020، صفحه 99-110 اصل مقاله (428.84 K)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22059/jchpe.2020.292454.1300|
|Vahid Mojjaradi* 1؛ Sadegh Sahraei2|
|1Department of Petroleum and Gas Engineering, Shahid Bahonar University of Kerman, P.O. Box 76175-133 Kerman Iran|
|2Department of Polymer Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran|
|This study aims to develop an Adaptive Network-based Fuzzy Inference System technique (ANFIS) and using the parameters of a complex mathematical model in the RO membrane performances. The ANFIS was constructed by using a subtractive clustering method to generate initial fuzzy inference systems. The model trained by 70% of the data set and then its validity is examined by remained 30% data set. The result indicated that this method could predict the performance of the RO membrane faster and more accurately than previous numerical techniques. The squared correlation coefficient between real data and predicted data of this technique was 0.9973 for separation factor, 0.9916 for NP and 0.9975 NT, which are better in comparison with numerical methods, and previous Artificial Neural network used by the author to model these membranes. It was observed that the squash factor, reject ratio, and accept ratio has no significant effect on ANFIS performance. Results showed that for all cases better performances achieved when this parameter has a value of more than 0.5, as 0.86 for separation factor, 0.91 for net pre flux, and 0.83 for total flux. This technique just takes a few seconds to model RO membrane performance which is very faster than other numerical methods. So, this technique could be a powerful method to predict RO membranes.|
|ANFIS؛ Membrane؛ RO Performances؛ Separation|
 Khulbe KC, Feng CY, Matsuura T. Synthetic Polymer Membranes. Germany: Springer; 2008.
 Baker RH, Membrane Technology and Application. 2nd Edition. California, USA: Wiley; 2004.
 Norman NL, Fane AG, Winston Ho WS, Matsuura T Matsuura, Advanced Membrane Technology
and Applications. California, USA: Wiley; 2008.
 Kavitha J, Rajalakshmi M, Phani AR, Padaki M. Pretreatment processes for seawater reverse
osmosis desalination systems—A review. Journal of Water Process Engineering. 2019 Dec
 Noble RD, Stern SA, editors. Membrane separations technology: principles and applications.
Elsevier; 1995 Jan 17.
 Hoffman EJ. Membrane separations technology: single-stage, multistage, and differential
permeation. Elsevier; 2003 May 12.
 Arola K, Van der Bruggen B, Mänttäri M, Kallioinen M. Treatment options for nanofiltration and
reverse osmosis concentrates from municipal wastewater treatment: A review. Critical Reviews in
Environmental Science and Technology. 2019 Nov 17;49(22):2049-116.
 Rho H, Chon K, Cho J. An autopsy study of a fouled reverse osmosis membrane used for ultrapure
water production. Water. 2019 Jun;11(6):1116.
 Soltanieh M, GILL' WN. Review of reverse osmosis membranes and transport models. Chemical
Engineering Communications. 1981 Nov 1;12(4-6):279-363.
 Jiang S, Li Y, Ladewig BP. A review of reverse osmosis membrane fouling and control strategies.
Science of the Total Environment. 2017 Oct 1;595:567-83.
 Chen C, Qin H. A Mathematical Modeling of the Reverse Osmosis Concentration Process of a
Glucose Solution. Processes. 2019 May;7(5):271.
 Stockie JM. Modelling and simulation of porous immersed boundaries. Computers & structures.
2009 Jun 1;87(11-12):701-9.
 Murthy ZV, Gupta SK. Estimation of mass transfer coefficient using a combined nonlinear
membrane transport and film theory model. Desalination. 1997 Mar 1;109(1):39-49.
 Barragán VM, Kjelstrup S. Thermo-osmosis in membrane systems: a review. Journal of Non-Equilibrium Thermodynamics. 2017 Jun 27;42(3):217-36.
 Kimura S, Sourirajan S. Analysis of data in reverse osmosis with porous cellulose acetate membranes used. AIChE Journal. 1967 May;13(3):497-503.
 Chan K, Matsuura T, Sourirajan S. Interfacial forces, average pore size, and pore size distribution of ultrafiltration membranes. Industrial & Engineering Chemistry Product Research and Development. 1982 Dec;21(4):605-12.
 Matsuura T, Sourirajan S. Reverse osmosis transport through capillary pores under the influence of surface forces. Industrial & Engineering Chemistry Process Design and Development. 1981 Apr;20(2):273-82.
 Moradi A, Farsi A, Mansouri SS, Sarcheshmehpoor M. A new approach for modeling of RO membranes using MD-SF-PF model and CFD technique. Research on Chemical Intermediates. 2012 Jan 1;38(1):161-77.
 Golnari A, Moradi A, Soltani A. Effects of different potential functions on modeling of RO membrane performance by use of an advanced model. Research on Chemical Intermediates. 2013 Jul 1;39(6):2603-19.
 Mehdizadeh H, Dickson JM. Theoretical modification of the surface force-pore flow model for reverse osmosis transport. Journal of membrane science. 1989 Mar 1;42(1-2):119-45.
 Ghernaout D. Reverse Osmosis Process Membranes Modeling—A Historical Overview. Journal of Civil, Construction and Environmental Engineering. 2017 Oct 9;2(4):112-22.
 Mehdizadeh H, Molaiee-Nejad K, Chong YC. Modeling of mass transport of aqueous solutions of multi-solute organics through reverse osmosis membranes in case of solute–membrane affinity: Part 1. Model development and simulation. Journal of Membrane Science. 2005 Dec 15;267(1-2):27-40.
 Golnari, A. Moradi, A. Soltani, The New Potential and Friction Functions in the MD-SF-PF Model for the Modeling of RO Membranes, 1th Iran membrane conference, (2005).
 Al-Shayji K, Liu YA. Neural networks for predictive modeling and optimization of large-scale commercial water desalination plants. InProc. IDA World Congress Desalination Water Science 1997 (Vol. 1, pp. 1-15).
 Al-Shayji KA, Liu YA. Predictive modeling of large-scale commercial water desalination plants: data-based neural network and model-based process simulation. Industrial & Engineering Chemistry Research. 2002 Dec 11;41(25):6460-74.
 Jafar MM, Zilouchian A. Adaptive receptive fields for radial basis functions. Desalination. 2001 Apr 20;135(1-3):83-91.
 Abdulbary AF, Lai LL, Reddy KV, Al-Gobaisi SM. Artificial neural networks as efficient tools of simulation. InProceedings of the IDA World Congress on Desalination and Water Sciences 1995.
 Abbas A, Al-Bastaki N. Modeling of an RO water desalination unit using neural networks. Chemical Engineering Journal. 2005 Nov 15;114(1-3):139-43.
 Ramasamy S, Deshpande PB, Tambe SS, Kulkarni BD. Identification and advanced controls of MSF desalination plants with neural networks. InProceedings of IDA World Congress. Desalination Water Sci 1995 (Vol. 1, pp. 36-51).
 Razavi MA, Mortazavi A, Mousavi M. Dynamic modelling of milk ultrafiltration by artificial neural network. Journal of Membrane Science. 2003 Aug 1;220(1-2):47-58.
 Razavi SM, Mousavi SM, Mortazavi SA. Dynamic prediction of milk ultrafiltration performance: A neural network approach. Chemical Engineering Science. 2003 Sep 1;58(18):4185-95.
 Shetty GR, Chellam S. Predicting membrane fouling during municipal drinking water nanofiltration using artificial neural networks. Journal of Membrane Science. 2003 Jun 1;217(1-2):69-86.
 Bowen WR, Jones MG, Welfoot JS, Yousef HN. Predicting salt rejections at nanofiltration membranes using artificial neural networks. Desalination. 2000 Jul 10;129(2):147-62.
 Liu QF, Kim SH. Evaluation of membrane fouling models based on bench-scale experiments: a comparison between constant flowrate blocking laws and artificial neural network (ANNs) model. Journal of Membrane Science. 2008 Mar 5;310(1-2):393-401.
 Shahsavand A, Chenar MP. Neural networks modeling of hollow fiber membrane processes. Journal of Membrane Science. 2007 Jul 5;297(1-2):59-73.
 Teodosiu C, Pastravanu O, Macoveanu M. Neural network models for ultrafiltration and backwashing. Water Research. 2000 Dec 15;34(18):4371-80.
 Jang JS. ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics. 1993 May;23(3):665-85.
 MathWorks. 2002. Fuzzy logic toolbox user’s guide, for use of the Matlab. The Math Works Inc. http://www.mathworks.com
 Chiu SL. Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems. 1994 Jan 1;2(3):267-78.
 Yager RR, Filev DP. Generation of fuzzy rules by mountain clustering. Journal of Intelligent & Fuzzy Systems. 1994 Jan 1;2(3):209-19.
تعداد مشاهده مقاله: 366
تعداد دریافت فایل اصل مقاله: 297