Artificial Intelligence–Based Optimization of Reverse Osmosis Systems Operation Performance
Publication: Journal of Environmental Engineering
Volume 146, Issue 2
Abstract
In recent years, reverse osmosis (RO) systems have been highly utilized in industrial processes. One of the most important operational issues of these systems is membrane fouling, which leads to high operating costs and environmental impacts. The purpose of this research is to optimize RO systems’ operation to reduce fouling, increase membrane life span, and minimize system costs. To achieve this purpose, first, RO system characteristics are simulated using a general regression neural network (GRNN) artificial neural network. Then, the controllable factors affecting the performance of the system are optimized by the application of a single-objective optimization model with the total operating cost minimization as an objective function. The proposed method is applied to an under-operation RO system used in a car manufacturer factory in Iran. Based on the results, the optimal values of the inflow, inlet pressure, and recovery rate were , 7.4 × 105 Pa, and 60%, respectively. Accordingly, the total operational cost of the system will be $1,525.95. Moreover, by an appropriate operation, the system can continue to work for more than 5,000 h without the need for cleaning.
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References
Abdelrasoul, A., H. Doan, and A. Lohi. 2013. “Fouling in membrane filtration and remediation methods.” In Mass transfer-advances in sustainable energy and environment oriented numerical modeling, edited by H. Nakajima. 1st ed. London: IntechOpen.
Affenzeller, M., S. Wagner, S. Winkler, and A. Beham. 2018. Genetic algorithms and genetic programming: Modern concepts and practical applications. 1st ed. New York: Chapman and Hall/CRC Press.
Aish, A. M., H. A. Zaqoot, and S. M. Abdeljawad. 2015. “Artificial neural network approach for predicting reverse osmosis desalination plants performance in the Gaza Strip.” Desalination. 367 (Jul): 240–247. https://doi.org/10.1016/j.desal.2015.04.008.
Al-Bahri, Z. K., W. T. Hanbury, and T. Hodgkiess. 2001. “Optimum feed temperatures for seawater reverse osmosis plant operation in an MSF/SWRO hybrid plant.” Desalination. 138 (1–3): 335–339. https://doi.org/10.1016/S0011-9164(01)00282-X.
Al-Obaidi, M. A., C. Kara-Zaitri, and I. M. Mujtaba. 2018. “Optimal reverse osmosis network configuration for the rejection of dimethylphenol from wastewater.” J. Environ. Eng. 144 (1): 04017080. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001284.
Araghinejad, S. 2013. Data-driven modeling: Using MATLAB® in water resources and environmental engineering. 1st ed. New Delhi, India: Springer.
Beyer, F., J. Laurinonyte, A. Zwijnenburg, A. J. Stams, and C. M. Plugge. 2017. “Membrane fouling and chemical cleaning in three full-scale reverse osmosis plants producing demineralized water.” J. Eng. 2017: 1–14. https://doi.org/10.1155/2017/6356751.
Byrne, W. 1995. Reverse osmosis: A practical guide for industrial users. 1st ed. Littleton, CO: Tall Oaks Publishing.
Cabrera, P., J. A. Carta, J. González, and G. Melián. 2017. “Artificial neural networks applied to manage the variable operation of a simple seawater reverse osmosis plant.” Desalination. 416 (Aug): 140–156. https://doi.org/10.1016/j.desal.2017.04.032.
Chaibva, F., M. Burton, and R. B. Walker. 2010. “Optimization of salbutamol sulfate dissolution from sustained release matrix formulations using an artificial neural network.” Pharmaceutics 2 (2): 182–198. https://doi.org/10.3390/pharmaceutics2020182.
Crittenden, J. C., R. R. Trussell, D. W. Hand, K. J. Howe, and G. Tchobanoglous. 2012. MWH’s water treatment: Principles and design. 3rd ed. Hoboken, NJ: Wiley.
CSM (Customer Satisfaction Membranes). 2006. “Technical manual: Reverse osmosis membranes.” Saehan Industries. Accessed August 13, 2018. www.csmfilter.com/searchfile/file/tech_manual.pdf.
Demeuse, M. T. 2009. “Production and applications of hollow fibers.” In Handbook of textile fiber structure, edited by S. J. Eichhorn, J. W. S. Hearle, M. Jaffe, and T. Kikutani. 1st ed. Cambridge, UK: Woodhead Publishing.
Dow Water and Process Solutions. 2011. “FILMTEC™ reverse osmosis membranes technical manual.” Accessed August 10, 2018. https://www.pdfdrive.com/filmtec-reverse-osmosis-membranes-technical-manual-d37880843.html.
El-Halwagi, M. M. 1992. “Synthesis of reverse-osmosis networks for waste reduction.” AlChE J. 38 (8): 1185–1198. https://doi.org/10.1002/aic.690380806.
Fazeli, A., R. Mehrabadi, J. Yadal, and M. Jafarzadeh. 2005. “Investigation of the use of ultraviolet ray to reduce the biological fouling of reverse osmosis membranes (Case study: Razi petrochemical complex).” [In Persian.] Modaress Tech. J. 27: 85–96.
Gil, J. D., A. Ruiz-Aguirre, L. Roca, G. Zaragoza, and M. Berenguel. 2018. “Prediction models to analyze the performance of a commercial-scale membrane distillation unit for desalting brines from RO plants.” Desalination 445 (Nov): 15–28. https://doi.org/10.1016/j.desal.2018.07.022.
Griffiths, K. A., and R. C. Andrews. 2011. “Application of artificial neural networks for filtration optimization.” J. Environ. Eng. 137 (11): 1040–1047. https://doi.org/10.1061/(ASCE)EE.1943-7870.0000439.
Guria, C., P. K. Bhattacharya, and S. K. Gupta. 2005. “Multi-objective optimization of reverse osmosis desalination units using different adaptations of the non-dominated sorting genetic algorithm (NSGA).” Comput. Chem. Eng. 29 (9): 1977–1995. https://doi.org/10.1016/j.compchemeng.2005.05.002.
Helal, A. M., A. M. El-Nashar, E. Al-Katheeri, and S. Al-Malek. 2003. “Optimal design of hybrid RO/MSF desalination plants Part I: Modeling and algorithms.” Desalination 154 (1): 43–66. https://doi.org/10.1016/S0011-9164(03)00207-8.
Huang, X., G. R. Guillen, and E. M. V. Hoek. 2010. “A new high-pressure optical membrane module for direct observation of seawater RO membrane fouling and cleaning.” J. Membrane Sci. 364 (1): 149–156. https://doi.org/10.1016/j.memsci.2010.08.009.
Hwang, T. M., Y. Choi, S. H. Nam, S. Lee, H. Oh, K. Hyun, and Y. K. Choung. 2010. “Prediction of membrane fouling rate by neural network modeling.” Desalin. Water Treat. 15 (1–3): 134–140. https://doi.org/10.5004/dwt.2010.1677.
Iritani, E. 2013. “A review on modeling of pore-blocking behaviors of membranes during pressurized membrane filtration.” Drying Technol. 31 (2): 146–162. https://doi.org/10.1080/07373937.2012.683123.
Jiang, A., S. J. Zhou, W. Cheng, J. Wang, Q. Ding, and C. Xing. 2015. “Operational optimization of SWRO process with the consideration of load fluctuation and electricity price.” IFAC-PapersOnLine. 48 (8): 598–604. https://doi.org/10.1016/j.ifacol.2015.09.033.
Jiang, S., Y. Li, and B. P. Ladewig. 2017. “A review of reverse osmosis membrane fouling and control strategies.” Sci. Total Environ. 595 (Oct): 567–583. https://doi.org/10.1016/j.scitotenv.2017.03.235.
Kotb, H., E. H. Amer, and K. A. Ibrahim. 2016. “On the optimization of RO (Reverse Osmosis) system arrangements and their operating conditions.” Energy. 103: 127–150. https://doi.org/10.1016/j.energy.2016.02.162.
Lu, Y., Y. Hu, D. Xu, and L. Wu. 2006. “Optimum design of reverse osmosis seawater desalination system considering membrane cleaning and replacing.” J. Membrane Sci. 282 (1): 7–13. https://doi.org/10.1016/j.memsci.2006.04.019.
Lu, Y., Y. Hu, X. Zhang, L. Wu, and Q. Liu. 2007. “Optimum design of reverse osmosis system under different feed concentration and product specification.” J. Membrane Sci. 287 (2): 219–229. https://doi.org/10.1016/j.memsci.2006.10.037.
Marcovecchio, M. G., P. A. Aguirre, and N. J. Scenna. 2005. “Global optimal design of reverse osmosis networks for seawater desalination: Modeling and algorithm.” Desalination 184 (1): 259–271. https://doi.org/10.1016/j.desal.2005.03.056.
McCall, J. 2005. “Genetic algorithms for modelling and optimization.” J. Comput. Appl. Math. 184 (1): 205–222. https://doi.org/10.1016/j.cam.2004.07.034.
Mgbe, C. O., J. M. Mom, and G. A. Igwue. 2015. “Performance evaluation of generalized regression neural network path loss prediction model in macrocellular environment.” JMEST. 2 (2): 204–208.
Mirbagheri, S. A., M. Bagheri, Z. Bagheri, and A. M. Kamarkhani. 2015. “Evaluation and prediction of membrane fouling in a submerged membrane bioreactor with simultaneous upward and downward aeration using artificial neural network-genetic algorithm.” Proc. Saf. Environ. Prot. 96: 111–124. https://doi.org/10.1016/j.psep.2015.03.015.
Mohammadi, T., M. Kazemimoghadam, and S. S. Madaeni. 2002. “Hydrodynamic factors affecting flux and fouling during reverse osmosis of seawater.” Desalination 151: 239.
Navarro, T. 2018. “Water reuse and desalination in Spain–challenges and opportunities.” J. Water Reuse Desalin. 8 (2): 153–168. https://doi.org/10.2166/wrd.2018.043.
Nazif, S., M. Karamouz, M. Tabesh, and A. Moridi. 2010. “Pressure management model for urban water distribution networks.” Water Resour. Manage. 24 (3): 437–458. https://doi.org/10.1007/s11269-009-9454-x.
Owens, E., and M. Patel. 2010. “RO cleaning frequency: A balance of costs.” Accessed December 25, 2017. https://www.wwdmag.com/sites/wwd/files/08_CaseStudy_Owens.pdf.
Palacin, L. G., F. Tadeo, C. de Prada, and J. Salazar. 2013. “Scheduling of the membrane module rotation in RO desalination plants.” Desalin. Water Treat. 51 (1–3): 352–359. https://doi.org/10.1080/19443994.2012.715077.
Park, K. B., C. Choi, H. W. Yu, S. R. Chae, and I. S. Kim. 2018. “Optimization of chemical cleaning for reverse osmosis membranes with organic fouling using statistical design tools.” Environ. Eng. Res. 23 (4): 474–484. https://doi.org/10.4491/eer.2017.098.
Peña, N., S. Gallego, F. Del Vigo, and S. P. Chesters. 2013. “Evaluating impact of fouling on reverse osmosis membranes performance.” Desalin. Water Treat. 51 (4–6): 958–968. https://doi.org/10.1080/19443994.2012.699509.
Reyhani, A., and H. M. Meighani. 2016. “Optimal operating conditions of micro-and ultra-filtration systems for produced-water purification: Taguchi method and economic investigation.” Desalin. Water Treat. 57 (42): 19642–19654. https://doi.org/10.1080/19443994.2015.1101714.
Reyhani, A., K. Sepehrinia, S. M. Seyed Shahabadi, F. Rekabdar, and A. Gheshlaghi. 2015. “Optimization of operating conditions in ultrafiltration process for produced water treatment via Taguchi methodology.” Desalin. Water Treat. 54 (10): 2669–2680. https://doi.org/10.1080/19443994.2014.904821.
Roehl, E. A., D. A. Ladner, R. C. Daamen, J. B. Cook, J. Safarik, D. W. Phipps, and P. Xie. 2018. “Modeling fouling in a large RO system with artificial neural networks.” J. Membr. Sci. 552 (Apr): 95–106. https://doi.org/10.1016/j.memsci.2018.01.064.
Salehi, F., M. A. Razavi, M. Elahi, and M. Noghabi. 2011. “Effect of operating parameters and feed concentration on the flux and fouling of sewage nano-filtration resulting from the recovery of color removing columns in the sugar industry.” [In Persian.] In Proc., 1st National Conf. on Membrane and Membrane Processes. Tehran, Iran: Jahad Daneshgahi.
Salinity, I. F. W. 2001. “Design parameters affecting performance.” Hydranautics. Accessed September 5, 2018. www.membranes.com/docs/trc/desparam.pdf.
Sannino, D., O. Sacco, and A. Chianese. 2013. “Determination of optimal operating condition in nanofiltration (NF) and reverse osmosis (RO) during the treatment of a tannery wastewater stream.” Chem. Engineer. Trans. 32: 1993–1998. https://doi.org/10.3303/CET1332333.
Sassi, K. M., and I. M. Mujtaba. 2011. “Optimal design and operation of reverse osmosis desalination process with membrane fouling.” Chem. Eng. J. 171 (2): 582–593. https://doi.org/10.1016/j.cej.2011.04.034.
Schmitt, F., R. Banu, I. T. Yeom, and K. U. Do. 2018. “Development of artificial neural networks to predict membrane fouling in an anoxic-aerobic membrane bioreactor treating domestic wastewater.” Biochem. Eng. J. 133 (May): 47–58. https://doi.org/10.1016/j.bej.2018.02.001.
Sharma, R. R., and S. Chellam. 2005. “Temperature effects on the morphology of porous thin film composite nanofiltration membranes.” Environ. Sci. Technol. 39 (13): 5022–5030. https://doi.org/10.1021/es0501363.
Sharma, R. R., and S. Chellam. 2006. “Temperature and concentration effects on electrolyte transport across porous thin-film composite nanofiltration membranes: Pore transport mechanisms and energetics of permeation.” J. Colloid. Interface Sci. 298 (1): 327–340. https://doi.org/10.1016/j.jcis.2005.12.033.
Soleimani, R., N. A. Shoushtari, B. Mirza, and A. Salahi. 2013. “Experimental investigation, modeling and optimization of membrane separation using artificial neural network and multi-objective optimization using genetic algorithm.” Chem. Eng. Res. Des. 91 (5): 883–903. https://doi.org/10.1016/j.cherd.2012.08.004.
Song, L., S. Hong, J. Y. Hu, S. L. Ong, and W. J. Ng. 2002. “Simulations of full-scale reverse osmosis membrane process.” J. Environ. Eng. 128 (10): 960–966. https://doi.org/10.1061/(ASCE)0733-9372(2002)128:10(960).
Thirugnanasambandham, K., V. Sivakumar, and K. Shine. 2016. “Optimization of reverse osmosis treatment process to reuse the distillery wastewater using Taguchi design.” Desalin. Water Treat. 57 (51): 24222–24230. https://doi.org/10.1080/19443994.2016.1141323.
Villafafila, A., and I. M. Mujtaba. 2003. “Fresh water by reverse osmosis based desalination: Simulation and optimisation.” Desalination. 155 (1): 1–13. https://doi.org/10.1016/S0011-9164(03)00234-0.
Vince, F., F. Marechal, E. Aoustin, and P. Breant. 2008. “Multi-objective optimization of RO desalination plants.” Desalination. 222 (1): 96–118. https://doi.org/10.1016/j.desal.2007.02.064.
Vrouwenvelder, J. S., and D. Van der Kooij. 2003. “Diagnosis of fouling problems of NF and RO membrane installations by a quick scan.” Desalination. 153 (1): 121–124. https://doi.org/10.1016/S0011-9164(02)01111-6.
Zarai, N., F. Tadeo, and M. Chaabene. 2013. “Planning of the operating points in desalination plants based on energy optimization.” Int. J. Comput. Appl. 68 (18): 6–11. https://doi.org/10.5120/11677-0878.
Zhang, Q., and S. J. Stanley. 1999. “Real-time water treatment process control with artificial neural networks.” J. Environ. Eng. 125 (2): 153–160. https://doi.org/10.1061/(ASCE)0733-9372(1999)125:2(153).
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Published In
Journal of Environmental Engineering
Volume 146 • Issue 2 • February 2020
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jun 30, 2018
Accepted: May 6, 2019
Published online: Nov 29, 2019
Published in print: Feb 1, 2020
Discussion open until: Apr 29, 2020
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