Simulation of Reverse Osmosis Process: Novel Approaches and Development Trends | Journal of Engineering Sciences

Simulation of Reverse Osmosis Process: Novel Approaches and Development Trends

Author(s): Huliienko S. V.1*, Korniyenko Y. M.1, Muzyka S. M.1, Holubka K.2

Affiliation(s): Department of Chemical and Petroleum Engineering, Bayero University, Kano 700281, Nigeria

*Corresponding Author’s Address: [email protected]

Issue: Volume 9, Issue 2 (2022)

Dates:
Submitted: May 8, 2022
Accepted for publication: September 9, 2022
Available online: September 12, 2022

Citation:
Huliienko S. V.,Korniyenko Y. M., Muzyka S. M.,Holubka K. (2022). Simulation of reverse osmosis process: Novel approaches and development trends. Journal of Engineering Sciences, Vol. 9(2), pp. F6-F36, doi: 10.21272/jes.2022.9(2).f2

DOI: 10.21272/jes.2022.9(2).f2

Research Area:  CHEMICAL ENGINEERING: Processes in Machines and Devices

Abstract. Reverse osmosis is an essential technological separation process that has a large number of practical applications. The mathematical simulation is significant for designing and determining the most effective modes of membrane equipment operation and for a deep understanding of the processes in membrane units. This paper is an attempt at systematization and generalizing the results of the investigations dedicated to reverse osmosis simulation, which was published from 2011 to 2020. The main approaches to simulation were analyzed, and the scope of use of each of them was delineated. It was defined that computational fluid dynamics was the most used technique for reverse osmosis simulation; the intensive increase in using of molecular dynamics methods was pointed out. Since these two approaches provide the deepest insight into processes, it is likely that they will further be widely used for reverse osmosis simulations. At the same time, for the simulation of the membrane plant, it is reasonable to use the models that required the simplest solutions methods. The solution-diffusion model appears to be the most effective and flexible for these purposes. Therefore, this model was widely used in considering the period. The practical problems solved using each of the considered approaches were reviewed. Moreover, the software used for the solution of the mathematical models was regarded.

Keywords: reverse osmosis, membrane, simulation, optimization, software.

Full Text

References:

  1. Huliienko S. V. Korniienko Y. M., Gatilov K. O. (2020). Modern trends in the mathematical simulation of pressure-driven membrane processes. Journal of Engineering Sciences, Vol. 7(1), pp. F1–F21, doi: https://doi.org/10.21272/jes.2020.7(1).f1  
  2. Jarzyńska M., Pietruszka M. (2011). The application of the Kedem–Katchalsky equations to membrane transport of ethyl alcohol and glucose. Desalination. Vol. 280, Issues 1–3, pp. 14-19, doi: https://doi.org/10.1016/j.desal.2011.07.034  
  3. Al-Obaidi M.A., Kara-Zaitri C., Mujtaba M. (2017). Scope and limitations of the irreversible thermodynamics and the solution diffusion models for the separation of binary and multi-component systems in reverse osmosis process. Computers & Chemical Engineering. Vol. 100, pp. 48-79, doi: https://doi.org/10.1016/j.compchemeng.2017.02.001  
  4. Qasim M., Badrelzaman M., Darwish N.N., Darwish N. A., Hilal N. (2019). Reverse osmosis desalination: A state-of-the-art review. Desalination. Volume 459, pp. 59-104, doi: https://doi.org/10.1016/j.desal.2019.02.008  
  5. Ahmed F. E., Hashaikeh R., Diabat A., Hilal N. (2019). Mathematical and optimization modelling in desalination: State-of-the-art and future direction. Desalination. Vol. 469, 114092, doi: https://doi.org/10.1016/j.desal.2019.114092  
  6. Karabelas A.J., Kostoglou M., Koutsou C.P. (2015). Modeling of spiral wound membrane desalination modules and plants – review and research priorities. Desalination. Vol. 356, pp. 165-186, doi: https://doi.org/10.1016/j.desal.2014.10.002  
  7. Keir, G., Jegatheesan, V. (2014). A review of computational fluid dynamics applications in pressure-driven membrane filtration. Reviews in Environmental Science and Bio/Technology. Vol. 13, pp. 183–201, doi: https://doi.org/10.1007/s11157-013-9327-x  
  8. Toh K. Y., Liang Y. Y., Lau W. J., Fimbres Weihs G. A. (2020). A Review of CFD Modelling and Performance Metrics for Osmotic Membrane Processes. Membranes. Vol. 10, Is. 10, 285, doi: https://doi.org/10.3390/membranes10100285  
  9. Cohen-Tanugi D., Grossman J. C. (2015). Nanoporous graphene as a reverse osmosis membrane: Recent insights from theory and simulation. Desalination. Vol. 366, pp. 59-70, doi: https://doi.org/10.1016/j.desal.2014.12.046  
  10. Ebro H., Kim Y. M., Kim J. H. (2013). Molecular dynamics simulations in membrane-based water treatment processes: A systematic overview. Journal of Membrane Science. Vol. 438, pp. 112-125, doi: https://doi.org/10.1016/j.memsci.2013.03.027  
  11. Ridgway H. F., Orbell J., Gray S. (2017). Molecular simulations of polyamide membrane materials used in desalination and water reuse applications: Recent developments and future prospects. Journal of Membrane Science. Vol. 524, pp. 436-448, doi: https://doi.org/10.1016/j.memsci.2016.11.061  
  12. Wang J., Dlamini D. S., Mishra A. K., Pendergast M. T. M., Wong M. C.Y., Mamba B. B., Freger V., Verliefde A. R.D., Hoek E. M.V. (2014). A critical review of transport through osmotic membranes. Journal of Membrane Science. Vol. 454, pp. 516-537, doi: https://doi.org/10.1016/j.memsci.2013.12.034  
  13. Ismail A. F., Matsuura T. (2018). Progress in transport theory and characterization method of Reverse Osmosis (RO) membrane in past fifty years. Desalination. Vol. 434, pp. 2-11, doi: https://doi.org/10.1016/j.desal.2017.09.028  
  14. Alsarayreh A. A., Al-Obaidi M. A., Patel R., Mujtaba I. M. (2020). Scope and Limitations of Modelling, Simulation, and Optimisation of a Spiral Wound Reverse Osmosis Process-Based Water Desalination. Processes. Vol. 8, Is. 5, 573, doi: https://doi.org/10.3390/pr8050573    
  15. Park K. Kim J., Yang D. R., Hong S. (2020). Towards a low-energy seawater reverse osmosis desalination plant: A review and theoretical analysis for future directions. Journal of Membrane Science. Vol. 595, 117607, doi: https://doi.org/10.1016/j.memsci.2019.117607  
  16. Abejón R., Garea A., Irabien A. (2012). Analysis, modelling and simulation of hydrogen peroxide ultrapurification by multistage reverse osmosis. Chemical Engineering Research and Design. Vol. 90, Is.3, pp. 442-452, doi: https://doi.org/10.1016/j.cherd.2011.07.025  
  17. Abejón R., Garea A., Irabien A. (2012). Integrated countercurrent reverse osmosis cascades for hydrogen peroxide ultrapurification, Computers & Chemical Engineering, Vol. 41, pp. 67-76, doi: https://doi.org/10.1016/j.compchemeng.2012.02.017  
  18. Fujioka T., Khan S. J., McDonald J. A., Roux A., Poussade Y., Drewes J. E., Nghiem L. D. (2014). Modelling the rejection of N-nitrosamines by a spiral-wound reverse osmosis system: Mathematical model development and validation, Journal of Membrane Science, Vol. 454, pp. 212-219, doi: https://doi.org/10.1016/j.memsci.2013.12.008  
  19. Zaghbani N., Nakajima M., Nabetani Hiroshi, Hafiane A. (2017). Modeling of reverse osmosis flux of aqueous solution containing glucose, Korean Journal of Chemical Engineering, Vol. 34, pp. 407–412, doi: https://doi.org/10.1007/s11814-016-0298-9  
  20. Ruiz-García A., Nuez I. (2016). Long-term performance decline in a brackish water reverse osmosis desalination plant. Predictive model for the water permeability coefficient, Desalination, Vol. 397, pp. 101-107, doi: https://doi.org/10.1016/j.desal.2016.06.027  
  21. Al-Obaidi M. A., Kara-Zaïtri C., Mujtaba I. M. (2016). Development and Validation of N-nitrosamine Rejection Mathematical Model Using a Spiral-wound Reverse Osmosis Process, Chemical engineering transactions, Vol. 52, pp. 1129-1134, doi: https://doi.org/10.3303/CET1652189  
  22. Al-Obaidi M.A., Kara-Zaïtri C., Mujtaba I.M. (2017). Removal of phenol from wastewater using spiral-wound reverse osmosis process: Model development based on experiment and simulation, Journal of Water Process Engineering, Vol. 18, pp. 20-28, doi: https://doi.org/10.1016/j.jwpe.2017.05.005  
  23. Karabelas A. J., Kostoglou M., Koutsou C. P. (2019). Advanced Dynamic Simulation of Membrane Desalination Modules Accounting for Organic Fouling, Journal of Membrane Science & Research, Vol. 5, Is. 2, pp. 178-186, doi: https://doi.org/10.22079/JMSR.2019.94172.1216  
  24. Kezia K., Lee J., Hill A. J., Kentish S. E. (2013). Convective transport of boron through a brackish water reverse osmosis membrane, Journal of Membrane Science, Vol. 445, pp. 160-169, doi: https://doi.org/10.1016/j.memsci.2013.05.041  
  25. Nir O., Lahav O. (2014). Modeling weak acids’ reactive transport in reverse osmosis processes: A general framework and case studies for SWRO, Desalination, Vol. 343, pp. 147-153, doi: https://doi.org/10.1016/j.desal.2013.11.009  
  26. Chen C., Qin H. (2019). A Mathematical Modeling of the Reverse Osmosis Concentration Process of a Glucose Solution, Processes, Vol. 7(5), 271, doi:  https://doi.org/10.3390/pr7050271   
  27. Al-Obaidi M.A., Kara-Zaïtri C., Mujtaba I.M. (2018). Simulation and optimisation of spiral-wound reverse osmosis process for the removal of N-nitrosamine from wastewater. Chemical Engineering Research and Design. Vol. 133, pp. 168-182, https://doi.org/10.1016/j.cherd.2018.03.012  
  28. Patroklou G., Sassi K. M., Mujtaba I. M. (2013). Simulation of Boron Rejection by Seawater Reverse Osmosis Desalination, Chemical engineering transactions, Vol. 32, pp. 1873-1878, doi: https://doi.org/10.3303/CET1332313  
  29. Arjmandia M., Chenar M. P., Altaee A., Arjmandi A., Peyravi M., Jahanshahi M., Binaeian E. (2020). Caspian seawater desalination and whey concentration through forward osmosis (FO)-reverse osmosis (RO) and FO-FO-RO hybrid systems: Experimental and theoretical study. Journal of Water Process Engineering. Vol. 37, 101492, doi: https://doi.org/10.1016/j.jwpe.2020.101492  
  30. Gaublomme D., Strubbe L., Vanoppen M., Torfs E., Mortier S., Cornelissen E., De Gusseme B., Verliefde A. Nopens I. (2020). A generic reverse osmosis model for full-scale operation. Desalination. Vol. 490, 114509, doi: https://doi.org/10.1016/j.desal.2020.114509  
  31. Ennasri H., Drighil A., Adhiri R., Fahli A., Moussetad M. (2019). Design and Simulation of a Solar Energy System for Desalination of Brackish Water. Environmental and Climate Technologies. Vol. 23, Is. 1, pp. 257–276, doi: https://doi.org/10.2478/rtuect-2019-0017  
  32. Al-Alawy A. F., Salih M. H. (2016). Experimental Study and Mathematical Modelling of Zinc Removal by Reverse Osmosis Membranes. Iraqi Journal of Chemical and Petroleum Engineering. Vol.17 No.3, pp. 57- 73.
  33. Sachit D. E. (2017). Effect of Several Parameters on Membrane Fouling by Using Mathematical Models of Reverse Osmosis Membrane System. Al-Nahrain Journal for Engineering Sciences. Vol.20 No.4, pp.864-870.
  34. Al-Obaidi M.A., Mujtaba I.M. (2016). Steady state and dynamic modeling of spiral wound wastewater reverse osmosis process. Computers & Chemical Engineering. Vol. 90, pp. 278-299, doi: https://doi.org/10.1016/j.compchemeng.2016.04.001  
  35. Al-Obaidi M.A., Kara- Zaïtri C., Mujtaba I.M. (2017). Optimum design of a multi-stage reverse osmosis process for the production of highly concentrated apple juice. Journal of Food Engineering. Vol. 214, pp. 47-59. doi: https://doi.org/10.1016/j.jfoodeng.2017.06.020  
  36. Al-Obaidi M.A., Li J-P., Kara-Zaïtri C., Mujtaba I.M. (2017). Optimisation of reverse osmosis based wastewater treatment system for the removal of chlorophenol using genetic algorithms. Chemical Engineering Journal. Vol. 316, pp. 91-100. doi: https://doi.org/10.1016/j.cej.2016.12.096  
  37. Al-Obaidi M.A., Kara-Zaïtri C., Mujtaba I.M. (2017). Development of a mathematical model for apple juice compounds rejection in a spiral-wound reverse osmosis process. Journal of Food Engineering. Vol. 192, pp.111-121, doi: https://doi.org/10.1016/j.jfoodeng.2016.08.005  
  38. Al-Obaidi M.A., Li J-P., Alsadaie S., Kara-Zaïtri C., Mujtaba I.M. (2018). Modelling and optimisation of a multistage Reverse Osmosis processes with permeate reprocessing and recycling for the removal of N-nitrosodimethylamine from wastewater using Species Conserving Genetic Algorithms. Chemical Engineering Journal. Vol. 350, pp. 824-834, doi: https://doi.org/10.1016/j.cej.2018.06.022  
  39. Altaee A. (2012). Computational model for estimating reverse osmosis system design and performance: Part-one binary feed solution. Desalination. Vol. 291, pp. 101-105, doi: https://doi.org/10.1016/j.desal.2012.01.028  
  40. Altaee A., Zaragoza G., van Tonningen H. R. (2014). Comparison between Forward Osmosis-Reverse Osmosis and Reverse Osmosis processes for seawater desalination. Desalination. Vol. 336, pp. 50-57, doi: https://doi.org/10.1016/j.desal.2014.01.002  
  41. Altaee A., Sharif A., Zaragoza G., Ismail A. F. (2015). Evaluation of FO-RO and PRO-RO designs for power generation and seawater desalination using impaired water feeds. Desalination. Vol. pp. 27-35, doi: https://doi.org/10.1016/j.desal.2014.06.022  
  42. Altaee A., Hilal N. (2015). High recovery rate NF–FO–RO hybrid system for inland brackish water treatment. Desalination. Vol. 363, pp. 19-25, doi: https://doi.org/10.1016/j.desal.2014.12.017  
  43. Ameri M., Eshaghi M. S. (2016). A novel configuration of reverse osmosis, humidification–dehumidification and flat plate collector: Modeling and exergy analysis. Applied Thermal Engineering. Vol. 103, pp. 855-873, doi: https://doi.org/10.1016/j.applthermaleng.2016.04.047  
  44. Barello M., Manca D., Patel R., Mujtaba I.M. (2015). Operation and modeling of RO desalination process in batch mode. Computers & Chemical Engineering. Vol. 83, pp. 139-156. doi: https://doi.org/10.1016/j.compchemeng.2015.05.022  
  45. Choi J.-S., Kim J.-T. (2015). Modeling of full-scale reverse osmosis desalination system: Influence of operational parameters. Journal of Industrial and Engineering Chemistry. Vol. 21, pp. 261-268, doi: https://doi.org/10.1016/j.jiec.2014.02.033  
  46. Fujioka T., Oshima N., Suzuki R., Price W. E., Nghiem L. D. (2015). Probing the internal structure of reverse osmosis membranes by positron annihilation spectroscopy: Gaining more insight into the transport of water and small solutes. Journal of Membrane Science. Vol. 486, pp. 106-118, doi: https://doi.org/10.1016/j.memsci.2015.02.007  
  47. Hung L.-Y., Lue S. J., You J.-H. (2011). Mass-transfer modeling of reverse-osmosis performance on 0.5–2% salty water. Desalination. Vol. 265, pp 67-73, doi: https://doi.org/10.1016/j.desal.2010.07.033  
  48. Jbari Y., Abderaf S. (2020). Parametric study to enhance performance of wastewater treatment process, by reverse osmosis‑photovoltaic system. Applied Water Science. Vol. 10, 217, doi: https://doi.org/10.1007/s13201-020-01301-4  
  49. Jiang A., Biegler L. T., Wang J., Cheng W., Ding Q., Jiangzhou S. (2015). Optimal operations for large-scale seawater reverse osmosis networks. Journal of Membrane Science. Vol. 476, pp. 508-524, doi: https://doi.org/10.1016/j.memsci.2014.12.005  
  50. Kim J., Park M., Shon H. K., Kim J. H. (2016). Performance analysis of reverse osmosis, membrane distillation, and pressure-retarded osmosis hybrid processes. Desalination. Vol. 380, pp. 85-92, doi: https://doi.org/10.1016/j.desal.2015.11.019  
  51. Álvarez J. R., Antón F. E., Álvarez-García S., Luque S. (2020). Treatment of Aqueous Effluents from Steel Manufacturing with High Thiocyanate Concentration by Reverse Osmosis. Membranes. Vol. 10, Is. 12, 437, doi: https://doi.org/10.3390/membranes10120437  
  52. Nir O., Lahav O. (2013). Coupling mass transport and chemical equilibrium models for improving the prediction of SWRO permeate boron concentrations. Desalination. Vol. 310, pp. 87-92, doi: https://doi.org/10.1016/j.desal.2012.09.001  
  53. Sundaramoorthy S., Srinivasan G., Murthy D.V.R. (2011). An analytical model for spiral wound reverse osmosis membrane modules: Part I — Model development and parameter estimation. Desalination. Vol. 280, Is. 1–3, pp. 403-411, doi: https://doi.org/10.1016/j.desal.2011.03.047  
  54. Gui S., Mai Z., Fu J., Wei Y., Wan J. (2020). Transport Models of Ammonium Nitrogen in Wastewater from Rare Earth Smelteries by Reverse Osmosis Membranes. Sustainability. Vol. 12, Is. 15, 6230, doi: https://doi.org/10.3390/su12156230  
  55. Wu X., Hu Y., Wu L., Li H. (2014). Model and Design of Cogeneration System for Different Demands of Desalination Water, Heat and Power Production. Chinese Journal of Chemical Engineering. Vol. 22, Is. 3, pp. 330-338, doi: https://doi.org/10.1016/S1004-9541(14)60036-7  
  56. Mai Z., Gui S., Fu J., Jiang C., Ortega E., Zhao Y., Tu W., Mickols W., Van der Bruggen B. (2019). Activity-derived model for water and salt transport in reverse osmosis membranes: A combination of film theory and electrolyte theory. Desalination, Vol. 469, 114094. doi: https://doi.org/10.1016/j.desal.2019.114094  
  57. Filippini G., Al-Obaidi M.A., Manenti F., Mujtaba I.M. (2018). Performance analysis of hybrid system of multi effect distillation and reverse osmosis for seawater desalination via modelling and simulation. Desalination, Vol. 448, pp. 21-35, doi: https://doi.org/10.1016/j.desal.2018.09.010  
  58. Al-Obaidi M.A., Alsarayreh A.A., Al-Hroub A.M., Alsadaie S., Mujtaba I.M. (2018). Performance analysis of a medium-sized industrial reverse osmosis brackish water desalination plant. Desalination. Vol. 443, pp. 272-284. doi: https://doi.org/10.1016/j.desal.2018.06.010  
  59. Ruiz-García A., de la Nuez Pestana I. (2019). Feed spacer geometries and permeability coefficients. Effect on the performance in BWRO spriral-wound membrane modules. Water. Vol. 11, 152. doi: https://doi.org/10.3390/w11010152  
  60. Ruiz-García A., de la Nuez Pestana I. (2018). A computational tool for designing BWRO systems with spiral wound modules. Desalination. Vol. 426, pp. 69-77. doi: http://dx.doi.org/10.1016/j.desal.2017.10.040  
  61.  Skiborowski M. Mhamdi A., Kraemer K., Marquardt W. (2012). Model-based structural optimization of seawater desalination plants. Desalination. Vol. 292, pp. 30-44. doi: http://dx.doi.org/10.1016/j.desal.2012.02.007  
  62. Gautam D. K., Teklu H., Subbiah S. (2020). Analysis of reverse osmosis process in hollow fiber module with and without secondary permeate outlet. Journal of Water Process Engineering. Vol. 36, 101336. doi: https://doi.org/10.1016/j.jwpe.2020.101336  
  63. Al-Obaidi M.A., Kara-Zaitri C., Mujtaba I.M. (2018). Performance evaluation of multi-stage and multi-pass reverse osmosis networks for the removal of N-nitrosodimethylamine -D6 (NDMA) from wastewater using model-based techniques. Journal of Environmental Chemical Engineering. Vol. 6, Is. 4, pp. 4797-4808. doi: https://doi.org/10.1016/j.jece.2018.06.014  
  64. Gu B. Xu X. Y., Adjiman C. S. (2017). A predictive model for spiral wound reverse osmosis membrane modules: The effect of winding geometry and accurate geometric details. Computers & Chemical Engineering. Vol. 96, pp. 248-265. doi: https://doi.org/10.1016/j.compchemeng.2016.07.029  
  65. Efraty A. (2016). CCD series no-22: Recent advances in RO, FO and PRO and their hybrid applications for high recovery desalination of treated sewage effluents. Desalination. Vol. 389, pp. 18-38. doi: http://dx.doi.org/10.1016/j.desal.2016.01.009  
  66. Kim J., Park K., Hong S. (2020). Optimization of two-stage seawater reverse osmosis membrane processes with practical design aspects for improving energy efficiency.  Journal of Membrane Science. Vol. 601, 117889. doi: https://doi.org/10.1016/j.memsci.2020.117889  
  67. Fraidenraich N., de Castro Vilela O., dos Santos Viana M., Gordon J. M. (2016). Improved analytic modeling and experimental validation for brackish-water reverse-osmosis desalination. Desalination. Vol. 380, pp.60-65. doi: http://dx.doi.org/10.1016/j.desal.2015.11.014  
  68. M.A., Kara-Zaitri C., Mujtaba I.M. (2019). Performance evaluation of multi-stage reverse osmosis process with permeate and retentate recycling strategy for the removal of chlorophenol from wastewater. Computers & Chemical Engineering. Vol. 121, pp. 12-26. doi: https://doi.org/10.1016/j.compchemeng.2018.08.035  
  69. Wang Q., Zhou Z., Li J., Tang Q., Hu Y. (2019). Investigation of the reduced specific energy consumption of the RO-PRO hybrid system based on temperature-enhanced pressure retarded osmosis. Journal of Membrane Science. Vol. 581, pp. 439-452. doi: https://doi.org/10.1016/j.memsci.2019.03.079  
  70. Kim J., Park M., Snyder Sh. A., Kim J. H. (2013). Reverse osmosis (RO) and pressure retarded osmosis (PRO) hybrid processes: Model-based scenario study. Desalination. Vol. pp. 121-130. doi: http://dx.doi.org/10.1016/j.desal.2013.05.010  
  71. Niewersch C., Rieth C., Hailemariam L., Oriol G. G., Warczok J. (2020). Reverse osmosis membrane element integrity evaluation using imperfection model. Desalination. Vol. 476, 114175. doi: https://doi.org/10.1016/j.desal.2019.114175  
  72. Zaidi S.M. J., Fadhillah F., Khan Z., Ismail A.F. (2015). Salt and water transport in reverse osmosis thin film composite seawater desalination membranes. Desalination. Vol. 368, pp. 202-213. doi: http://dx.doi.org/10.1016/j.desal.2015.02.026  
  73. Yao S., Ji M. (2020). A small RO and MCDI coupled seawater desalination plant and its performance simulation analysis and optimization. Processes. 2020, Vol. 8(8), 944. doi: https://doi.org/10.3390/pr8080944  
  74. Sassi K., Mujtaba I. (2011). Optimal design of reverse osmosis based desalination process with seasonal variation of feed temperature. Chemical Engineering Transactions. Vol. 25, pp. 1055-1060. doi: https://doi.org/10.3303/CET1125176  
  75. Sassi K., Mujtaba I. (2011). Optimal design and operation of reverse osmosis desalination process with membrane fouling. Chemical Engineering Journal. Vol. 171, Is. 2, pp. 582-593. doi: https://doi.org/10.1016/j.cej.2011.04.034  
  76. Park K., Heo H., Kim D. Y., Yang D. R. (2018). Feasibility study of a forward osmosis/crystallization/reverse osmosis hybrid process with high-temperature operation: Modeling, experiments, and energy consumption. Journal of Membrane Science. Vol. 555, pp. 206-219. doi: https://doi.org/10.1016/j.memsci.2018.03.031  
  77. Venkata Swamy B., Madhumala M., Prakasham R.S., Sridhar S. (2013). Nanofiltration of bulk drug industrial effluent using indigenously developed functionalized polyamide membrane. Chemical Engineering Journal. Vol. 233, pp. 193-200. doi: https://doi.org/10.1016/j.cej.2013.08.045  
  78. Golnari A., Moradi A., Soltani A. (2013). Effects of different potential functions on modeling of RO membrane performance by use of an advanced model. Research on Chemical Intermediates. Vol. 39, pp. 2603–2619. doi: https://doi.org/10.1007/s11164-012-0784-6  
  79. Moradi A., Mojarradi V., Sarcheshmehpour M. (2013). Prediction of RO membrane performances by use of artificial neural network and using the parameters of a complex mathematical model. Research on Chemical Intermediates. Vol. 39, pp. 3235–3249. doi: https://doi.org/10.1007/s11164-012-0835-z  
  80. Madsen H. T., Søgaard E. G. (2014). Applicability and modelling of nanofiltration and reverse osmosis for remediation of groundwater polluted.  with pesticides and pesticide transformation products. Separation and Purification Technology. Vol. 125, pp. 111-119. doi: http://dx.doi.org/10.1016/j.seppur.2014.01.038  
  81. Takeuchi Sh., Tazaki A., Miyauchi S., Kajishim T. (2019). A relation between membrane permeability and flow rate at low Reynolds number in circular pipe. Journal of Membrane Science. Vol. 582, pp. 91-102. doi: https://doi.org/10.1016/j.memsci.2019.03.018  
  82. Merdaw A.A., Sharif A.O., Derwish G.A.W. (2011). Mass transfer in pressure-driven membrane separation processes, Part I. Chemical Engineering Journal. Vol. 168, pp. 215-228. doi: https://doi.org/10.1016/j.cej.2010.12.071  
  83. Rohlfs W., Thiel G. P., Lienhard V J. H. (2016). Modeling reverse osmosis element design using superposition and an analogy to convective heat transfer. Journal of Membrane Science. Vol. 512, pp. 38-49. doi: http://dx.doi.org/10.1016/j.memsci.2016.03.049  
  84.  Kavianipour O., Ingram G. D., Vuthaluru H. B. (2017). Investigation into the effectiveness of feed spacer configurations for reverse osmosis membrane modules using Computational Fluid Dynamics. Journal of Membrane Science. Vol. 526, pp. 156-171. doi: http://dx.doi.org/10.1016/j.memsci.2016.12.034
  85. Anqi A. E., Alkhamis N., Oztekin A. (2015). Numerical simulation of brackish water desalination by a reverse osmosis membrane. Desalination. Vol. 369, pp. 156-164. doi: http://dx.doi.org/10.1016/j.desal.2015.05.007  
  86. Anqi A. E., Alrehili M., Usta M., Oztekin A. (2016). Numerical analysis of hollow fiber membranes for desalination. Desalination. Vol. 398, pp. 39-51. doi: http://dx.doi.org/10.1016/j.desal.2016.07.019  
  87.  Anqi A. E., Alkhamis N., Oztekin A. (2016). Steady three dimensional flow and mass transfer analyses for brackish water desalination by reverse osmosis membranes. International Journal of Heat and Mass Transfer. Vol. 101, pp. 399-411. doi: http://dx.doi.org/10.1016/j.ijheatmasstransfer.2016.05.102  
  88. Anqi A. E., Alkhamis N., Oztekin A. (2016). Computational study of desalination by reverse osmosis – Three-dimensional analyses. Desalination. Vol. 388, pp. 38-49. doi: http://dx.doi.org/10.1016/j.desal.2016.03.017  
  89. Hamdache A., Belkacem M. (2018). Efects of a zero normal‑concentration‑gradient outfow boundary condition on concentration polarization in a CFD study of a reverse osmosis process. Journal of the Brazilian Society of Mechanical Sciences and Engineering. Vol. 40, 507. doi: https://doi.org/10.1007/s40430-018-1430-z  
  90. Jogdand A., Chaudhuri A. (2015). Modeling of concentration polarization and permeate flux variation in a roto-dynamic reverse osmosis filtration system. Desalination. Vol. 375, pp. 54-70. doi: http://dx.doi.org/10.1016/j.desal.2015.07.011  
  91.  Min J., Zhang B. (2014). Numerical Studies of Convective Mass Transfer Enhancement in a Membrane Channel by Rectangular Winglets. Chinese Journal of Chemical Engineering. Vol. 22, pp. 1061-1071. doi: http://dx.doi.org/10.1016/j.cjche.2014.09.004  
  92. Mojab S. M., Pollard A., Pharoah J. G., Beale S. B., Hanff E. S. (2014). Unsteady Laminar to Turbulent Flow in a Spacer-Filled Channel. Flow, Turbulence and Combustion. Vol. 92, pp. 563–577. doi: http://dx.doi.org/10.1007/s10494-013-9514-4  
  93. Ratnayake P., Setiawan R., Bao J., Fimbres-Weihs G., Wiley D. E. (2016). Spatio-temporal frequency response analysis of forced slip velocity effect on solute concentration oscillations in a reverse osmosis membrane channel. Computers & Chemical Engineering. Vol. 84, pp. 151-161. doi: http://dx.doi.org/10.1016/j.compchemeng.2015.08.016  
  94. Rohlfs W., Lienhard V J. H. (2016). Entrance length effects on Graetz number scaling in laminar duct flows with periodic obstructions: Transport number correlations for spacer-filled membrane channel flows. International Journal of Heat and Mass Transfer. Vol. 97, pp. 842-852. doi: http://dx.doi.org/10.1016/j.ijheatmasstransfer.2016.02.078  
  95. Saeed A., Vuthaluru R., Vuthaluru H. B. (2015). Investigations into the effects of mass transport and flow dynamics of spacer filled membrane modules using CFD. Chemical Engineering Research and Design. Vol. 93, pp. 79-99. doi: http://dx.doi.org/10.1016/j.cherd.2014.07.002  
  96. Sousa P., Soares A., Monteiro E., Rouboa A. (2014). A CFD study of the hydrodynamics in a desalination membrane filled with spacers. Desalination. Vol. 349, pp. 22-30. doi: http://dx.doi.org/10.1016/j.desal.2014.06.019  
  97. Usta M., Anqi A. E., Oztekin A. (2017). Reverse osmosis desalination modules containing corrugated membranes – Computational study. Desalination. Vol. 416, pp. 129-139. doi: http://dx.doi.org/10.1016/j.desal.2017.05.005  
  98. Kaufman Y., Kasher R., Lammertink R. G.H., Freger V. (2012). Microfluidic NF/RO separation: Cell design, performance and application. Journal of Membrane Science. Vol. 396, pp. 67-73. doi: http://doi.org/10.1016/j.memsci.2011.12.052  
  99.  Abdelbaky M. M. A., El‑Refaee M. M. (2019). A 3D CFD comparative study between torsioned and non‑torsioned net‑type feed spacer in reverse osmosis. SN Applied Sciences. Vol. 1, 1059. doi: https://doi.org/10.1007/s42452-019-1098-8  
  100. Jeong K., Park M., Ohd S., Kim J. H. (2020). Impacts of flow channel geometry, hydrodynamic and membrane properties on osmotic backwash of RO membranes—CFD modeling and simulation. Desalination. Vol. 476, 114229. doi: https://doi.org/10.1016/j.desal.2019.114229  
  101. Luo J., Lie M., Heng Y. (2020). A hybrid modeling approach for optimal design of non-woven membrane channels in brackish water reverse osmosis process with high-throughput computation. Desalination. Vol. 489, 114463. doi: https://doi.org/10.1016/j.desal.2020.114463  
  102. Bucs Sz.S., Radu A.I., Lavric V., Vrouwenvelder J.S., Picioreanu C. (2014). Effect of different commercial feed spacers on biofouling of reverse osmosis membrane systems: A numerical study. Desalination. Vol. 343, pp. 26-37. doi: http://dx.doi.org/10.1016/j.desal.2013.11.007  
  103. Gu B., Adjiman C. S., Xu X. Y. (2017). The effect of feed spacer geometry on membrane performance and concentration polarisation based on 3D CFD simulations. Journal of Membrane Science. Vol. 527, pp. 78-91. doi: http://dx.doi.org/10.1016/j.memsci.2016.12.058  
  104. Haaksman V. A., Siddiqui A., Schellenberg C., Kidwell J., Vrouwenvelder J. S., Picioreanu C. (2017). Characterization of feed channel spacer performance using geometries obtained by X-ray computed tomography. Journal of Membrane Science. Vol. 522, pp. 124-139. doi: http://dx.doi.org/10.1016/j.memsci.2016.09.005  
  105.  Horstmeyer N., Lippert T., Schön D., Schlederer F., Picioreanu C., Achterhold K., Pfeiffer F., Drewes J. E. (2018). CT scanning of membrane feed spacers – Impact of spacer model accuracy on hydrodynamic and solute transport modeling in membrane feed channels. Journal of Membrane Science. Vol. 564, pp 133-145. doi: https://doi.org/10.1016/j.memsci.2018.07.006  
  106. Gogar R., Vaseghi G., Lipscomb G. (2019). Comparisons of Experimental and Simulated Velocity Fields in Membrane Module Spacers. Journal of Membrane Science & Research. Vol. 5, Is. 4, pp. 283-294. doi: https://doi.org/10.22079/JMSR.2019.101683.1242  
  107. Kerdi S., Qamar A., Alpatova A., Vrouwenvelder J. S., Ghaffour N. (2020). Membrane filtration performance enhancement and biofouling mitigation using symmetric spacers with helical filaments. Desalination. Vol. 484, pp. 114454. doi: https://doi.org/10.1016/j.desal.2020.114454  
  108. Kostoglou M., Karabelas A. J. (2013). Comprehensive simulation of flat-sheet membrane element performance in steady state desalination. Desalination. Vol. 316, pp. 91-102. doi: http://dx.doi.org/10.1016/j.desal.2013.01.033  
  109. Kostoglou M., Karabelas A.J. (2016). Dynamic operation of flat sheet desalination-membrane elements: A comprehensive model accounting for organic fouling. Computers & Chemical Engineering. Vol. 93, pp. 1-12. doi: http://dx.doi.org/10.1016/j.compchemeng.2016.06.001  
  110. Koutsou C. P., Karabelas A. J. (2015). A novel retentate spacer geometry for improved spiral wound membrane (SWM) module performance. Journal of Membrane Science. Vol. 488, pp. 129-142. doi: http://dx.doi.org/10.1016/j.memsci.2015.03.064  
  111. Lee Y. K., Won Y.-J., Yoo J. H., Ahn K. H., Lee C.-H. (2013). Flow analysis and fouling on the patterned membrane surface. Journal of Membrane Science. Vol. 427, pp. 320-325. doi: http://dx.doi.org/10.1016/j.memsci.2012.10.010  
  112. Li M., Bui T., Chao S. (2016). Three-dimensional CFD analysis of hydrodynamics and concentration polarization in an industrial RO feed channel. Desalination. Vol. 397, pp. 194-204. doi:  http://dx.doi.org/10.1016/j.desal.2016.07.005  
  113. Mansouri N., Moghimi M., Taherinejad M. (2019). Investigation on hydrodynamics and mass transfer in a feed channel of a spiral-wound membrane element using response surface methodology. Chemical Engineering Research and Design. Vol. 149, pp. 147-157. doi: https://doi.org/10.1016/j.cherd.2019.07.006  
  114. Shoukat G., Ellahi F., Sajid M., Uddin E. (2020). Computational Study of Zigzag Spacer Design with Elliptical Cross-Section Filaments. MATEC Web of Conferences. Vol. 307, 01047. doi: https://doi.org/10.1051/matecconf/202030701047  
  115. Minelli M., Baschetti M. G., Doghieri F. (2011). A comprehensive model for mass transport properties in nanocomposites. Journal of Membrane Science. Vol. 381, Is. 1–2, pp. 10-20. doi: https://doi.org/doi:10.1016/j.memsci.2011.06.036  
  116. Motevalian S. P., Borhan A., Zhou H., Zydney A. (2016). Twisted hollow fiber membranes for enhanced mass transfer. Journal of Membrane Science. Vol. 514, pp. 586-594. doi: http://dx.doi.org/10.1016/j.memsci.2016.05.027  
  117. Park J., Lee K. S. (2017). A two-dimensional model for the spiral wound reverse osmosis membrane module. Desalination. Vol. 416, pp. 157-165. doi: http://dx.doi.org/10.1016/j.desal.2017.05.006  
  118. Qamar A., Bucs S., Picioreanu C., Vrouwenvelder J., Ghaffour N. (2019). Hydrodynamic flow transition dynamics in a spacer filled filtration channel using direct numerical simulation. Journal of Membrane Science. Vol. 590, 117264. doi: https://doi.org/10.1016/j.memsci.2019.117264  
  119. Ronen A., Lerman S., Ramon G. Z., Dosoretz C. G. (2015). Experimental characterization and numerical simulation of the anti-biofuling activity of nanosilver-modified feed spacers in membrane filtration. Journal of Membrane Science. Vol. 475, pp. 320-329. doi: http://dx.doi.org/10.1016/j.memsci.2014.10.042  
  120. Toh K.Y., Liang Y.Y., Lau W.J., Fimbres Weihs G.A. (2020). 3D CFD study on hydrodynamics and mass transfer phenomena for SWM feed spacer with different floating characteristics. Chemical Engineering Research and Design. Vol. 159, pp. 36-46. doi: https://doi.org/10.1016/j.cherd.2020.04.010  
  121. Usta M., Morabito M., Anqi A., Alrehili M., Hakim A., Oztekin A. (2018). Twisted hollow fiber membrane modules for reverse osmosis-driven desalination. Desalination. Vol. 441, pp. 21-34. doi: https://doi.org/10.1016/j.desal.2018.04.027  
  122. Qi J., Lv J., Li Z., Bian W., Li J., Liu S. (2020). A Numerical Simulation of Membrane Distillation Treatment of Mine Drainage by Computational Fluid Dynamics. Water. Vol. 12, Is. 12., 3403. doi: https://doi.org/10.3390/w12123403  
  123. Xie P., Murdoch L. C., Ladner D. A. (2014). Hydrodynamics of sinusoidal spacers for improved reverse osmosis performance. Journal of Membrane Science. Vol. 453, pp. 92-99. doi: http://dx.doi.org/10.1016/j.memsci.2013.10.068  
  124. Yang Zh., Cheng J., Yang C., Liang B. (2016). CFD-based optimization and design of multi-channel inorganic membrane tubes. Chinese Journal of Chemical Engineering. Vol.  24, Is. 10, pp. 1375-1385. doi: http://dx.doi.org/10.1016/j.cjche.2016.05.044  
  125. Zhuang L., Guo H., Wang P., Dai G. (2015). Study on the flux distribution in a dead-end outside-in hollow fiber membrane module. Journal of Membrane Science. Vol. 495, pp. 372-383. doi: http://dx.doi.org/10.1016/j.memsci.2015.07.060  
  126. Zhuang L., Guo H., Dai G., Xu Z. (2017). Effect of the inlet manifold on the performance of a hollow fiber membrane module-A CFD study. Journal of Membrane Science. Vol. 526, pp. 73-93. doi: http://dx.doi.org/10.1016/j.memsci.2016.12.018  
  127. Uppu A., Chaudhuri A., Das Sh. P., Prakash N. (2020). CFD modeling of gypsum scaling in cross-flow RO filters using moments of particle population balance. Journal of Environmental Chemical Engineering. Vol. 8, Is. 5, 104151. doi: https://doi.org/10.1016/j.jece.2020.104151  
  128. Liang Y.Y., Fimbres Weihs G.A., Fletcher D.F. (2018). CFD study of the effect of unsteady slip velocity waveform on shear stress in membrane systems. Chemical Engineering Science. Vol. 192, pp. 16-24. doi: https://doi.org/10.1016/j.ces.2018.07.009  
  129. Liang Y.Y., Fimbres Weihs G.A., Wiley D.E. (2020). Comparison of oscillating flow and slip velocity mass transfer enhancement in spacer-filled membrane channels: CFD analysis and validation. Journal of Membrane Science. Vol. 593, 117433. doi: https://doi.org/10.1016/j.memsci.2019.117433  
  130. Liang Y.Y., Toh K.Y., Fimbres Weihs G.A. (2019). 3D CFD study of the effect of multi-layer spacers on membrane performance under steady flow. Journal of Membrane Science. Vol. 580, pp. 256-267. doi: https://doi.org/10.1016/j.memsci.2019.02.015  
  131. Lim S.Y., Liang Y.Y., Fimbres Weihs G.A., Wiley D.E., Fletcher D.F. (2018). A CFD study on the effect of membrane permeance on permeate flux enhancement generated by unsteady slip velocity. Journal of Membrane Science. Vol. 556, pp. 138-145. doi: https://doi.org/10.1016/j.memsci.2018.03.070  
  132. Onorato C., Gaedtke M., Kespe M., Nirschl H., Schäfer A. I. (2019). Renewable energy powered membrane technology: Computational fluid dynamics evaluation of system performance with variable module size and fluctuating energy. Separation and Purification Technology. Vol. 220, pp. 206-216. doi: https://doi.org/10.1016/j.seppur.2019.02.041  
  133. Qi B., Wang Y., Wang Z., Zhang Y., Xu Sh., Wang Sh. (2013). Theoretical Investigation on Internal Leakage and Its Effect on the Efficiency of Fluid Switcher-Energy Recovery Device for Reverse Osmosis Desalting Plant. Chinese Journal of Chemical Engineering. Vol. 21, Is. 11, pp. 1216-1223. doi: https://doi.org/10.1016/S1004-9541(13)60625-4  
  134. Foo K., Liang Y.Y., Fimbres Weihs G.A. (2020). CFD study of the effect of SWM feed spacer geometry on mass transfer enhancement driven by forced transient slip velocity. Journal of Membrane Science. Vol. 597, 117643. doi: https://doi.org/10.1016/j.memsci.2019.117643  
  135. Gruber M. F., Aslak U., Hélix-Nielsen C. (2016). Open-source CFD model for optimization of forward osmosis and reverse osmosis membrane modules. Separation and Purification Technology. Vol. 158, pp. 183-192. doi: http://dx.doi.org/10.1016/j.seppur.2015.12.017  
  136. Ahmed S., Taif Seraji M., Jahedi J., Hashi M.A. (2012). Application of CFD for simulation of a baffled tubular membrane. Chemical Engineering Research and Design. Vol. 90, Is. 5, pp. 600-608. doi: http://dx.doi.org/doi:10.1016/j.cherd.2011.08.024  
  137.  Haddadi B., Jordan C., Miltner M., Harasek M. (2018). Membrane modeling using CFD: Combined evaluation of mass transfer and geometrical influences in 1D and 3D. Journal of Membrane Science. Vol. 563, pp. 199-209. doi: https://doi.org/10.1016/j.memsci.2018.05.040  
  138. Kaya R., Deveci G., Turken T., Sengur R., Guclu S., Koseoglu-Imer D.Y., Koyuncu I. (2014). Analysis of wall shear stress on the outside-in type hollow fiber membrane modules by CFD simulation. Desalination. Vol. 351, pp. 109-119. doi: http://dx.doi.org/10.1016/j.desal.2014.07.033  
  139. Wu S.-E., Lin Y.-Ch., Hwang K.-J., Cheng T.-W., Tung K.-L. (2018). High-efficiency hollow fiber arrangement design to enhance filtration performance by CFD simulation. Chemical Engineering and Processing – Process Intensification. Vol. 125, pp. 87-96. doi: https://doi.org/10.1016/j.cep.2018.01.003  
  140. Li W., Su X., Palazzolo A., Ahmed Sh., Thomas E. (2017). Reverse osmosis membrane, seawater desalination with vibration assisted reduced inorganic fouling. Desalination. Vol. 417, pp. 102-114. doi: http://dx.doi.org/10.1016/j.desal.2017.05.016  
  141. Kavianipour O., Ingram G. D., Vuthaluru H. B. (2019). Studies into the mass transfer and energy consumption of commercial feed spacers for RO membrane modules using CFD: Effectiveness of performance measures. Chemical Engineering Research and Design. Vol. 141, pp. 328-338. doi: https://doi.org/10.1016/j.cherd.2018.10.041  
  142. Naskar M., Rana K., Chatterjee D., Dhara T., Sultana R., Sarkar D. (2019). Design, performance characterization and hydrodynamic modeling of intermeshed spinning basket membrane (ISBM) module. Chemical Engineering Science. Vol. 206, pp. 446-462. doi: https://doi.org/10.1016/j.ces.2019.05.049  
  143. Ahmed I., Hussain A., Hasani S.M.F., Shakaib M., Yunus R. M. (2012). Computational modeling for visualization of flow patterns in a membrane testing device. Separation and Purification Technology. Vol. 90, pp. 1-9. doi: https://doi.org/doi:10.1016/j.seppur.2012.02.004  
  144. Chaumeil F., Crapper M. (2013).  DEM simulations of initial deposition of colloidal particles around non-woven membrane spacers. Journal of Membrane Science. Vol. 442, pp. 254-263. doi: http://dx.doi.org/10.1016/j.memsci.2013.04.031  
  145. Saeed A., Vuthaluru R., Yang Y., Vuthaluru H. B. (2012). Effect of feed spacer arrangement on flow dynamics through spacer filled membranes. Desalination. Vol. 285, pp. 163-169. doi: https://doi.org/10.1016/j.desal.2011.09.050  
  146. Fimbres Weihs G.A., Wiley D.E. (2014). CFD analysis of tracer response technique under cake-enhanced osmotic pressure. Journal of Membrane Science. Vol. 449, pp. 38-49. doi: http://dx.doi.org/10.1016/j.memsci.2013.08.015  
  147. Srivathsan G., Sparrow E. M., Gorman J. M. (2014). Reverse osmosis issues relating to pressure drop, mass transfer, turbulence, and unsteadiness. Desalination. Vol. 341, pp. 83-86. doi: https://doi.org/10.1016/j.desal.2014.02.021  
  148. Wypysek D., Rall D., Wiese M., Neef T., Koops G.-H., Wessling M. (2019). Shell and lumen side flow and pressure communication during permeation and filtration in a multibore polymer membrane module. Journal of Membrane Science. Vol. 584, pp. 254-267. doi: https://doi.org/10.1016/j.memsci.2019.04.070  
  149. Taherinejad M., Moghimi M., Derakhshan Sh. (2019). Hydrodynamic modeling of the spiral-wound membrane module including the membrane curvature: reverse osmosis case study. Korean Journal of Chemical Engineering. Vol. 36, pp. 2074–2084. doi: https://doi.org/10.1007/s11814-019-0372-1  
  150. Ligaray M., Kim N.-H., Park S., Park J.-S., Park J. Kim Y., Cho K. H. (2020). Energy projection of the seawater battery desalination system using the reverse osmosis system analysis model. Chemical Engineering Journal. Vol. 395, pp. 125082. doi: https://doi.org/10.1016/j.cej.2020.125082  
  151. Taherinejad M., Derakhshan Sh., Yavarinasab A. (2017). Hydrodynamic analysis of spiral wound reverse osmosis membrane recovery fraction and permeate water flow rate. Desalination. Vol. 411, pp. 59-68. doi: http://dx.doi.org/10.1016/j.desal.2017.02.009  
  152. Nejati S., Mirbagheri S. A., Warsinger D. M., Fazeli M. (2019). Biofouling in seawater reverse osmosis (SWRO): Impact of module geometry and mitigation with ultrafiltration. Journal of Water Process Engineering. Vol. 29, 100782. doi: https://doi.org/10.1016/j.jwpe.2019.100782  
  153. Johannink M., Masilamani K., Mhamdi A., Roller S., Marquardt W. (2015). Predictive pressure drop models for membrane channels with non-woven and woven spacers. Desalination. Vol. 376, pp. 41-54. doi: http://dx.doi.org/10.1016/j.desal.2015.07.024  
  154. Palomar P., Lara J.L., Losada I.J., Rodrigo M., Alvárez A. (2012). Near field brine discharge modelling part 1: Analysis of commercial tools. Desalination. Vol. 290, pp. 14-27. doi: https://doi.org/10.1016/j.desal.2011.11.037  
  155. Haidaria A.H., Heijman S.G.J., van der Meer W.G.J. (2018). Optimal design of spacers in reverse osmosis. Separation and Purification Technology. Vol. 192, pp. 441-456. doi: https://doi.org/10.1016/j.seppur.2017.10.042  
  156. Karabelas A.J., Koutsou C.P., Kostoglou M. (2014). The effect of spiral wound membrane element design characteristics on its performance in steady state desalination — A parametric study. Desalination. Vol. 332, Is. 1, 2 pp. 76-90. doi: http://dx.doi.org/10.1016/j.desal.2013.10.027  
  157. Koutsou C.P., Karabelas A.J., Kostoglou M. (2014). Membrane desalination under constant water recovery – The effect of module design parameters on system performance. Separation and Purification Technology. Vol. 147, pp. 90-113. doi: http://dx.doi.org/10.1016/j.seppur.2015.04.012  
  158. Barello M., Manca D., Patel R., Mujtaba I.M. (2014). Neural network based correlation for estimating water permeability constant in RO desalination process under fouling. Desalination. Vol. 345, pp. 101-111. doi: http://dx.doi.org/10.1016/j.desal.2014.04.016  
  159. Farahbakhsh J., Delnavaz M., Vatanpour V. (2019). Simulation and characterization of novel reverse osmosis membrane prepared by blending polypyrrole coated multiwalled carbon nanotubes for brackish water desalination and antifouling properties using artificial neural networks. Journal of Membrane Science. Vol. 581, pp. 123-138. doi: https://doi.org/10.1016/j.memsci.2019.03.050  
  160. Salami E. S., Ehetshami M., Karimi-Jashni A., Salari M., Nikbakht Sheibani S., Ehteshami A. (2016). A mathematical method and artificial neural network modeling to simulate osmosis membrane’s performance. Modeling Earth Systems and Environment. Vol. 2, pp. 1–11. doi: https://doi.org/10.1007/s40808-016-0261-0  
  161. Mohammad A. Th., Al-Obaidi, M. A., Hameed E. M., Basheer B. N., Mujtab I. M. (2020). Modelling the chlorophenol removal from wastewater via reverse osmosis process using a multilayer artificial neural network with genetic algorithm. Journal of Water Process Engineering. Vol. 33, 100993. doi: https://doi.org/10.1016/j.jwpe.2019.100993  
  162. Aish A. M., Zaqoot H. A., Abdeljawad S. M. (2015). Artificial neural network approach for predicting reverse osmosis desalination plants performance in the Gaza Strip. Desalination. Vol. 367, pp. 240-247. doi: http://dx.doi.org/10.1016/j.desal.2015.04.008  
  163. Jbari Y., Abderaf S. (2020). Parametric study to enhance performance of wastewater treatment process, by reverse osmosis-photovoltaic system. Applied Water Science. Vol. 10, 217. doi: https://doi.org/10.1007/s13201-020-01301-4  
  164. Gu J., Luo J., Lif M., Huang C., Heng Y. (2020). Modeling of pressure drop in reverse osmosis feed channels using multilayer artificial neural networks. Chemical Engineering Research and Design. Vol. 159, pp. 146-156. doi: https://doi.org/10.1016/j.cherd.2020.04.019  
  165. Rall D., Schweidtmann A. M., Kruse M., Evdochenko E., Mitsos A., Wessling M. (2020). Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning. Journal of Membrane Science. Vol. 608, 118208. doi: https://doi.org/10.1016/j.memsci.2020.118208  
  166. Khayet M., Cojocaru C., Essalhi M. (2011). Artificial neural network modeling and response surface methodology of desalination by reverse osmosis. Journal of Membrane Science. Vol. 368, Is. 1–2, pp. 202-214. doi: https://doi.org/10.1016/j.memsci.2010.11.030  
  167. Park S., Baek S.-S., Pyo J. C. Pachepsky Y., Park J., Cho K. H. (2019). Deep neural networks for modeling fouling growth and flux decline during NF/RO membrane filtration. Journal of Membrane Science. Vol. 587, 117164. doi: https://doi.org/10.1016/j.memsci.2019.06.004  
  168. Roehl E. A., Ladner D. A., Daamen R. C., Cook J. B., Safarik J., Phipps D. W., Xie P. (2018). Modeling fouling in a large RO system with artificial neural networks. Journal of Membrane Science. Vol. 552, pp. 95-106. doi: https://doi.org/10.1016/j.memsci.2018.01.064  
  169. Cabrera P., Carta J. A., González J., Melián G. (2018). Wind-driven SWRO desalination prototype with and without batteries: A performance simulation using machine learning models. Desalination. Vol. 435, pp. 77-96. doi: https://doi.org/10.1016/j.desal.2017.11.044  
  170. Sargolzaei J., Haghighi Asl M., Hedayati Moghaddam A. (2012). Membrane permeate flux and rejection factor prediction using intelligent systems. Desalination. Vol. 284, pp. 92-99. doi: https://doi.org/10.1016/j.desal.2011.08.041  
  171. Azamat, J., Khataee, A. Joo, S.W. (2014) Separation of a heavy metal from water through a membrane containing boron nitride nanotubes: molecular dynamics simulations. Journal of Molecular Modeling. Vol. 20, 2468. doi: https://doi.org/10.1007/s00894-014-2468-1  
  172. Talati S., Mohebbi A., Dorrani H. (2019). Investigation of the Capability of Carbon Nanotube Membranes in Separating the Heavy Metal Ions from Aqueous Solutions by Molecular Dynamics Simulation. Journal of Engineering Thermophysics Vol. 28, pp. 123–137. doi: https://doi.org/10.1134/S1810232819010107  
  173. Hinkle K. R., Wang X., Gu X., Jameson C. J., Murad S. (2018). Computational Molecular Modeling of Transport Processes in Nanoporous Membranes. Processes. Vol. 6. Is. 8. 124. doi:  https://doi.org/10.3390/pr6080124  
  174. Boateng L. K., Madarshahian R., Yoon Y. Caicedo J. M., Flora J. R. V. (2016). A probabilistic approach for estimating water permeability in pressure-driven membranes. Journal of Molecular Modeling. Vol. 22, 185. doi: https://doi.org/10.1007/s00894-016-3049-2  
  175. Gao W., She F., Zhang J., Dumée L. F., He L., Hodgson P. D., Kong L. (2015). Understanding water and ion transport behaviour and permeability through poly(amide) thin film composite membrane. Journal of Membrane Science. Vol. 487, pp. 32-39. doi: https://doi.org/10.1016/j.memsci.2015.03.052  
  176. Li J., Kong X., Lu D., Liu Zh. (2015). Italicized carbon nanotube facilitating water transport: a molecular dynamics simulation. Science Bulletin. Vol. 60, Is.18, pp. 1580-1586. doi: https://doi.org/10.1007/s11434-015-0888-7  
  177. Shen J.-W., Li J., Liu F., Zhang L., Liang L., Wang H., Wu J.-Y. (2020). A molecular dynamics study on water desalination using single-layer MoSe2 nanopore. Journal of Membrane Science. Vol. 595, 117611. doi: https://doi.org/10.1016/j.memsci.2019.117611  
  178. Zheng B., Tian Y., Jia Sh., Zhao X., Li H. (2020). Molecular dynamics study on applying layered graphene oxide membranes for separating cadmium ions from water. Journal of Membrane Science. Vol. 603, 117996. doi: https://doi.org/10.1016/j.memsci.2020.117996  
  179. Shen M., Keten S., Lueptow R. M. (2016). Rejection mechanisms for contaminants in polyamide reverse osmosis membranes. Journal of Membrane Science. Vol. 509, pp. 36-47. doi: https://doi.org/10.1016/j.memsci.2016.02.043  
  180. Shen M., Keten S., Lueptow R. M. (2016). Dynamics of water and solute transport in polymeric reverse osmosis membranes via molecular dynamics simulations. Journal of Membrane Science. Vol. 506, pp. 95-108. doi: https://doi.org/10.1016/j.memsci.2016.01.051  
  181. Kiat Ng C., Domilongo Bope C., Nalaparaju A., Cheng Y., Lu L., Wang R., Cao B. (2016). Concentrating synthetic estrogen 17α-ethinyl estradiol using microporous polyethersulfone hollow fiber membranes: Experimental exploration and molecular simulation. Chemical Engineering Journal. Vol. 314, pp. 80-87. doi: https://doi.org/10.1016/j.cej.2016.12.109  
  182. Li T., Tu Q., Li Sh. (2019). Molecular dynamics modeling of nano-porous centrifuge for reverse osmosis desalination. Desalination. Vol. 451, pp. 182-191. doi: https://doi.org/10.1016/j.desal.2017.09.015  
  183. Zhao Z., Jiang J. (2020). POC/PIM-1 mixed-matrix membranes for water desalination: A molecular simulation study. Journal of Membrane Science. Vol. 608, 118173. doi: https://doi.org/10.1016/j.memsci.2020.118173  
  184. Luo Y., Harder E., Faibish R. S., Roux B. (2011). Computer simulations of water flux and salt permeability of the reverse osmosis FT-30 aromatic polyamide membrane. Journal of Membrane Science. Vol. 384, Is. 1–2, pp. 1-9. doi: https://doi.org/10.1016/j.memsci.2011.08.057  
  185. Lyu Q., Kang D.-Y., Hu S., Lin L.-Ch. (2020). Exploiting interior surface functionalization in reverse osmosis desalination membranes to mitigate permeability–selectivity trade-off: Molecular simulations of nanotube-based membranes. Desalination. Vol. 491, 114537. doi: https://doi.org/10.1016/j.desal.2020.114537  
  186. Chen Q., Yang X. (2015). Pyridinic nitrogen doped nanoporous graphene as desalination membrane: Molecular simulation study. Journal of Membrane Science. Vol. 496, pp. 108-117. doi: https://doi.org/10.1016/j.memsci.2015.08.052  
  187.  Surblys D., Yamada T., Thomsen B., Kawakami T., Shigemoto I., Okabe J., Ogawa T., Kimura M., Sugita Y., Yagi K. (2020). Amide A band is a fingerprint for water dynamics in reverse osmosis polyamide membranes. Journal of Membrane Science. Vol. 596, 117705. doi: https://doi.org/10.1016/j.memsci.2019.117705  
  188. Ji W.M., Zhang L.W. (2019). Molecular dynamics simulations of water desalination through polymerized fullerite membrane. Journal of Membrane Science. Vol. 576, pp. 108-115. doi: https://doi.org/10.1016/j.memsci.2019.01.028  
  189. Yao Y., Li M., Cao X., Zhang P., Zhang W., Zheng J., Zhang X., Wang L. (2018).  A novel sulfonated reverse osmosis membrane for seawater desalination: Experimental and molecular dynamics studies. Journal of Membrane Science. Vol. 550, pp. 470-479. doi: https://doi.org/10.1016/j.memsci.2018.01.023  
  190. Tomohisa Y., Kotaka K., Nakagawa K., Shintani T., Wu H.-Ch., Matsuyama H., Fujimura Y., Kawakatsu T. (2018). Molecular dynamics simulation study of polyamide membrane structures and RO/FO water permeation properties. Membranes. Vol. 8, Is. 4, 127. doi: https://doi.org/10.3390/membranes8040127
  191. Rizzuto C., Pugliese G., Bahattab M. A., Aljlil S. A., Drioli E., Tocci E. (2018). Multiwalled carbon nanotube membranes for water purification. Separation and Purification Technology. Vol. 193, pp. 378-385. doi: https://doi.org/10.1016/j.seppur.2017.10.025  
  192. Ding M., Szymczyk A., Goujon F., Soldera A., Ghouf A. (2014). Structure and dynamics of water confined in a polyamide reverse-osmosis membrane: A molecular-simulation study. Journal of Membrane Science. Vol. 458, pp. 236-244. doi:  http://dx.doi.org/10.1016/j.memsci.2014.01.054  
  193. Ding M., Ghoufi A., Anthony S. (2014). Molecular simulations of polyamide reverse osmosis membranes. Desalination. Vol. 343, pp. 48-53. doi: https://doi.org/10.1016/j.desal.2013.09.024  
  194. Ding M., Szymczyk A., Ghouf A. (2015). On the structure and rejection of ions by a polyamide membrane in pressure-driven molecular dynamics simulations. Desalination. Vol. 368, pp. 76-80. doi: http://dx.doi.org/10.1016/j.desal.2015.01.003  
  195. Ding M., Szymczyk A., Ghouf A. (2016). Hydration of a polyamide reverse-osmosis membrane. Journal of Membrane Science. Vol. 501, pp. 248-253. doi: http://dx.doi.org/10.1016/j.memsci.2015.12.036  
  196. Azamat J., Baghbani N. B., Erfan-Niya H. (2020). Atomistic understanding of functionalized γ-graphyne-1 nanosheet membranes for water desalination. Journal of Membrane Science. Vol. 604, 118079. doi: https://doi.org/10.1016/j.memsci.2020.118079  
  197. Yang H., Baek J., Park H. G. (2020). Architecture and mass transport properties of graphene‑based membranes. JMST Advances. Vol. 2, pp. 77–88. doi: https://doi.org/10.1007/s42791-020-00032-6  
  198. Zhu Y., Zhou J., Lu X., Guo X., Lu L. (2013). Molecular simulations on nanoconfined water molecule behaviors for nanoporous material applications. Microfluidics and Nanofluidics. Vol. 15, pp. 191–205. doi: https://doi.org/10.1007/s10404-013-1143-7  
  199. Müller E. A. (2013). Purification of water through nanoporous carbon membranes: a molecular simulation viewpoint. Current Opinion in Chemical Engineering. Vol. 2, Is. 2, pp. 223-228. doi: http://dx.doi.org/10.1016/j.coche.2013.02.004  
  200. Nguyen Ch. Th., Beskok A. (2020). Water desalination performance of h-BN and optimized charged graphene membranes. Microfluidics and Nanofluidics. Vol. 24, 39. doi: https://doi.org/10.1007/s10404-020-02340-8  
  201. Song Y., Wei M., Xu F., Wang Y. (2020).  Molecular simulations of water transport resistance in polyamide RO membranes: interfacial and interior contributions. Engineering. Vol. 6, Is. 5, pp. 577-584. doi: https://doi.org/10.1016/j.eng.2020.03.008   
  202. Karavas Ch.-S., Arvanitis K. G., Papadakis G. (2019). Optimal technical and economic configuration of photovoltaic powered reverse osmosis desalination systems operating in autonomous mode. Desalination. Vol. 466, pp. 97-106. doi: https://doi.org/10.1016/j.desal.2019.05.007  
  203. Atia A. A., Fthenakis V. (2019). Active-salinity-control reverse osmosis desalination as a flexible load resource. Desalination. Vol. 468, 114062. doi: https://doi.org/10.1016/j.desal.2019.07.002  
  204. Al-Obaidi M.A., Kara-Zaïtri C., Mujtaba I.M. (2019). Economic removal of chlorophenol from wastewater using multi-stage spiral-wound reverse osmosis process: Simulation and optimisation. Journal of Water Process Engineering. Vol. 31, 100829. doi: https://doi.org/10.1016/j.jwpe.2019.100829  
  205. Abejón A., Garea A., Irabien A. (2015). Arsenic removal from drinking water by reverse osmosis: Minimization of costs and energy consumption. Separation and Purification Technology. Vol. 144, pp. 46-53. doi: https://doi.org/10.1016/j.seppur.2015.02.017  
  206. Alnouri S., Linke P. (2013). Optimal SWRO network synthesis and design assessment with water quality insights. Chemical Engineering Transactions. Vol. 35, pp. 1225-1230. doi: https://doi.org/10.3303/CET1335204  
  207. Alnouri S. Y., Linke P. (2013). Optimal SWRO desalination network synthesis using multiple water quality parameters. Journal of Membrane Science. Vol. 444, pp. 493-512. doi: https://doi.org/10.1016/j.memsci.2013.04.066  
  208. Alnouri S. Y., Linke P. (2014). Optimal seawater reverse osmosis network design considering product water boron specifications. Desalination. Vol. 345, pp. 112-127. doi: https://doi.org/10.1016/j.desal.2014.04.030
  209. Jiang A., Wang J., Biegler L. T., Cheng W., Xing Ch., Jiang Zh. (2015). Operational cost optimization of a full-scale SWRO system under multi-parameter variable conditions. Desalination. Vol. 355, pp. 124-140. doi: https://doi.org/10.1016/j.desal.2014.10.016  
  210. Xu D., Acker T., Zhang X. (2019). Size optimization of a hybrid PV/wind/diesel/battery power system for reverse osmosis desalination. Journal of Water Reuse and Desalination. Vol. 9, Is. 4, pp. 405–422. doi: https://doi.org/10.2166/wrd.2019.019  
  211. Khor Ch. S., Chachuat B., Shah N. (2012). A superstructure optimization approach for water network synthesis with membrane separation-based regenerators. Computers & Chemical Engineering. Vol. 42, pp. 48-63. doi: https://doi.org/10.1016/j.compchemeng.2012.02.020  
  212. Maalouf S., Rosso D., Yeh W. W.-G. (2014). Optimal planning and design of seawater RO brine outfalls under environmental uncertainty. Desalination. Vol. 333, Is. 1, pp. 134-145. doi: https://doi.org/10.1016/j.desal.2013.11.015  
  213. Maleki A. (2018). Design and optimization of autonomous solar-wind-reverse osmosis desalination systems coupling battery and hydrogen energy storage by an improved bee algorithm. Desalination. Vol. 435, pp. 221-234. doi: https://doi.org/10.1016/j.desal.2017.05.034  
  214. Malik S. N., Bahri P. A., Vu L. T.T. (2016). Steady state optimization of design and operation of desalination systems using Aspen Custom Modeler. Computers & Chemical Engineering. Vol. 91, pp. 247-256. doi: https://doi.org/10.1016/j.compchemeng.2016.04.024
  215. Toth A. J. (2020). Modelling and optimisation of multi-stage flash distillation and reverse osmosis for desalination of saline process wastewater sources. Membranes. Vol. 10, Is. 10, 265. doi:  https://doi.org/10.3390/membranes10100265  
  216. Peng W., Maleki A., Rosend M. A., Azarikhah P. (2018). Optimization of a hybrid system for solar-wind-based water desalination by reverse osmosis: Comparison of approaches. Desalination. Vol. 442, pp. 16-31. doi: https://doi.org/10.1016/j.desal.2018.03.021  
  217. Prathapaneni D. R., Detroja K. (2020). Optimal design of energy sources and reverse osmosis desalination plant with demand side management for cost-effective freshwater production. Desalination. Vol. 496, 114741. doi: https://doi.org/10.1016/j.desal.2020.114741  
  218. Sassi K. M., Mujtaba I. M. (2013). MINLP based superstructure optimization for boron removal during desalination by reverse osmosis.  Journal of Membrane Science. Vol. 440, pp. 29-39. doi: https://doi.org/10.1016/j.memsci.2013.03.012  
  219. Senthil S., Senthilmurugan S. (2016). Reverse Osmosis–Pressure Retarded Osmosis hybrid system: Modelling, simulation and optimization. Desalination. Vol. 389, pp. 78-97. doi: https://doi.org/10.1016/j.desal.2016.01.027  
  220. Zebbar M., Messlem Y., Gouichiche A., Tadjine M. (2019). Super-twisting sliding mode control and robust loop shaping design of RO desalination process powered by PV generator. Desalination. Vol. 458, pp. 122-135. doi: https://doi.org/10.1016/j.desal.2019.02.011  
  221. Ruiz-García A., Nuez I., Carrascosa-Chisvert M.D., Santana J.J. (2020). Simulations of BWRO systems under different feedwater characteristics. Analysis of operation windows and optimal operating points. Desalination. Vol. 491, 114582. doi: https://doi.org/10.1016/j.desal.2020.114582  
  222. Jiang A., Wang J., Cheng W., Xing Ch., Jiangzhou Sh. (2014). A Dynamic Optimization Strategy for the Operation of Large Scale Seawater Reverses Osmosis System. Mathematical Problems in Engineering. Volume 2014, ID 635434. doi: https://doi.org/10.1155/2014/635434  
  223.  Bdour M., Dalala Z., Al-Addous M., Kharabsheh A., Khzouz H. (2020). Mapping RO-Water Desalination System Powered by Standalone PV System for the Optimum Pressure and Energy Saving. Applied Sciences. Vol. 10, Is. 6, 2161. doi: https://doi.org/10.3390/app10062161  
  224. Bitaw T. N., Park K., Yang D. R. (2016). Optimization on a new hybrid Forward osmosis-Electrodialysis-Reverse osmosis seawater desalination process. Desalination. Vol. 398, pp. 265-281. doi: https://doi.org/10.1016/j.desal.2016.07.032  
  225. Dimitriou E., Boutikos P., Mohamed E. Sh., Koziel S., Papadakis G. (2017). Theoretical performance prediction of a reverse osmosis desalination membrane element under variable operating conditions. Desalination. Vol. 419, pp. 70-78. doi: https://doi.org/10.1016/j.desal.2017.06.001  
  226. Kim J., Hong S. (2018). Optimizing seawater reverse osmosis with internally staged design to improve product water quality and energy efficiency. Journal of Membrane Science. Vol. 568, pp. 76-86. doi: https://doi.org/10.1016/j.memsci.2018.09.046
  227. Li M. (2012). Optimal plant operation of brackish water reverse osmosis (BWRO) desalination. Desalination. Vol. 293, pp. 61-68. doi: https://doi.org/10.1016/j.desal.2012.02.024  
  228. Weaver N. J., Wilkin G. S., Morison K. R., Watson M. J. (2020). Minimizing the energy requirements for the production of maple syrup. Journal of Food Engineering. Vol. 273, 109823. doi: https://doi.org/10.1016/j.jfoodeng.2019.109823  
  229. Almansoori A., Saif Y. (2014). Structural optimization of osmosis processes for water and power production in desalination applications.  Desalination. Vol. 344, pp. 12-27. doi: https://doi.org/10.1016/j.desal.2014.03.002   
  230. Al-Aboosi F. Y., El-Halwagi M. M. (2019). A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty. Sustainability. Vol. 11, Is. 18, 4865. doi: https://doi.org/10.3390/su11184865  
  231. Blankert B., Kim Y., Vrouwenvelder H., Ghaffour N. (2020). Facultative hybrid RO-PRO concept to improve economic performance of PRO: Feasibility and maximizing efficiency. Desalination. Vol. 478, 114268. doi: https://doi.org/10.1016/j.desal.2019.114268  
  232. Nematzadeh, M., Samimi, A., Shokrollahzadeh, S., Mohebbi-Kalhori, D. (2019). Bentazon removal from aqueous solution by reverse osmosis; optimization of effective parameters using response surface methodology. Advances in Environmental Technology. Vol. 5, Is. 4, pp. 193-201. doi: https://doi.org/10.22104/aet.2020.4228.1209  
  233. Al-Obaidi M.A., Kara-Zaïtri C., Mujtaba I.M. (2018). Simulation and optimisation of a two-stage/two-pass reverse osmosis system for improved removal of chlorophenol from wastewater. Journal of Water Process Engineering. Vol. 22, pp. 131-137. doi: https://doi.org/10.1016/j.jwpe.2018.01.012  
  234. Emamjome A., Zahedi M. M., Ziyaadini M. (2019). Economic analysis for process optimization of Chabahar Maritime University reverse osmosis desalination plant: a case study. Applied Water Science. Vol. 9, 114. doi: https://doi.org/10.1007/s13201-019-0995-8  
  235. Mirghaderi F., Rahmanian N., Patel R., Manca D., Mujtaba I. M. (2017). Simulation and Optimization of a Continuous Reverse Osmosis Desalination Process for Making Fresh Water. Chemical Engineering Transactions. Vol. 61, pp. 1783-1788. doi: https://doi.org/10.3303/CET1761295  
  236. Emad A., Ajbar A., Almutaz I. (2012). Periodic control of a reverse osmosis desalination process. Journal of Process Control. Vol. 22, Is. 1, pp. 218-227. doi: https://doi.org/10.1016/j.jprocont.2011.09.001  
  237. Kelley L. C., Dubowsky S. (2013). Thermal control to maximize photovoltaic powered reverse osmosis desalination systems productivity. Desalination. Vol. 314, pp. 10-19. doi: https://doi.org/10.1016/j.desal.2012.11.036  
  238. Volpin F., Fons E., Chekli L., Kim J. E., Jang A., Shon H. K. (2018). Hybrid forward osmosis-reverse osmosis for wastewater reuse and seawater desalination: Understanding the optimal feed solution to minimise fouling. Process Safety and Environmental Protection. Vol. 117, pp. 523-532. doi: https://doi.org/10.1016/j.psep.2018.05.006  
  239. Peters Ch. D., Hankins N. P. (2019). Osmotically assisted reverse osmosis (OARO): Five approaches to dewatering saline brines using pressure-driven membrane processes. Desalination. Vol. 458, pp. 1-13. doi: https://doi.org/10.1016/j.desal.2019.01.025  
  240. Antipova E., Pozo C., Guillén-Gosálbez G., Boer D., Cabeza L.F., Jiménez L. (2015). On the use of filters to facilitate the post-optimal analysis of the Pareto solutions in multi-objective optimization. Computers & Chemical Engineering. Vol. 74, pp. 48-58. doi: https://doi.org/10.1016/j.compchemeng.2014.12.012  
  241. Khoshgoftar Manesh M.H., Ghalami H., Amidpour M., Hamedi M.H. (2013). Optimal coupling of site utility steam network with MED-RO desalination through total site analysis and exergoeconomic optimization. Desalination. Vol. 316, pp. 42-52. doi: https://doi.org/10.1016/j.desal.2013.01.022  
  242. Sadri S., Khoshkhoo R.H., Ameri M. (2016). Multi objective optimization of reverse osmosis desalination plant with exergy approach. Journal of Mechanical Science and Technology. Vol. 30, pp. 4807–4814. doi: https://doi.org/10.1007/s12206-016-0953-4  
  243.  Al-Obaidi M. A., Kara-Zaïtri C., Mujtaba I. M. (2018). Statistical-Based Modeling and Optimization of Chlorophenol Removal from Wastewater Using Reverse Osmosis Process. Chemical Engineering Transactions. Vol. 70, pp. 2023-2028. doi: https://doi.org/10.3303/CET1870338  
  244. Stillwell A. S., Webber M. E. (2016). Predicting the Specific Energy Consumption of Reverse Osmosis Desalination. Water. Vol. 8, Is. 12, 601. doi: https://doi.org/10.3390/w8120601  
  245. Manenti F., Nadezhdin I. S., Goryunov A. G., Kozin K. A., Baydali S. A., Papasidero D., Rossi F., Potemin R. V. (2015). Operational Optimization of Reverse Osmosis Plant Using MPC. Chemical Engineering Transactions. Vol. 45. pp. 247-252. doi: https://doi.org/10.3303/CET1545042  
  246. Gong M., Jiang A., Zhang Q., Wang H., Hu J., Lin Y. (2017). An Improved Finite Element Meshing Strategy for Dynamic Optimization Problems. Mathematical Problems in Engineering. Vol. 2017, 4829195. doi: https://doi.org/10.1155/2017/4829195  
  247. Patnana N., Pattnaik S., Varshney T., Singh V. P. (2020). Self-Learning Salp Swarm Optimization Based PID Design of Doha RO Plant. Algorithms. Vol. 13, Is. 11, 287. doi: https://doi.org/10.3390/a13110287  
  248. Li D., Yang N., Niu R., Qiu H., Xi Y. (2012). FPGA based QDMC control for reverse-osmosis water desalination system. Desalination. Vol. 285, pp. 83-90. doi: https://doi.org/10.1016/j.desal.2011.09.037  
  249. Sobana S., Panda R. C. (2014). Modeling and control of reverse osmosis desalination process using centralized and decentralized techniques. Desalination. Vol. 344, pp, 243-251. doi: https://doi.org/10.1016/j.desal.2014.03.014  
  250. Ehteram M., Salih S.Q., Yaseen, Z.M. (2020). Efficiency evaluation of reverse osmosis desalination plant using hybridized multilayer perceptron with particle swarm optimization. Environmental Science and Pollution Research. Vol. 27, pp. 15278–15291. doi: https://doi.org/10.1007/s11356-020-08023-9  
  251. Jeong K., Park M., Ki S. J., Kim J. H. (2017). A systematic optimization of Internally Staged Design (ISD) for a full-scale reverse osmosis process. Journal of Membrane Science. Vol. 540, pp. 285-296. doi: https://doi.org/10.1016/j.memsci.2017.06.066  
  252. Jeong K., Park M., Chong T. H. (2019). Numerical model-based analysis of energy-efficient reverse osmosis (EERO) process: Performance simulation and optimization. Desalination. Vol. 453, pp. 10-21. doi: https://doi.org/10.1016/j.desal.2018.11.021  
  253. Fellah B., Benyoucef B., Chermiti A., Belarbi M., Amara S. (2018). Optimal sizing of a hybrid photovoltaic/wind system supplying a desalination unit. Journal of Engineering Science and Technology. Vol. 13, No. 6, pp. 1816-1833  
  254. Ghobeity A., Mitsos A. (2014). Optimal design and operation of desalination systems: new challenges and recent advances. Current Opinion in Chemical Engineering. Vol. 6, pp.  61-68. doi: https://doi.org/10.1016/j.coche.2014.09.008  
  255. Sano Y., Mahidul I. (2018) Optimum operating condition of a hollow fiber reverse osmosis desalination system. Cogent Engineering. Vol. 5, Is. 1, 1463898. doi: https://doi.org/10.1080/23311916.2018.1463898  
  256. Davies P.A. (2011). A solar-powered reverse osmosis system for high recovery of freshwater from saline groundwater. Desalination. Vol. 271, Is. 1–3, pp. 72-79. doi: https://doi.org/10.1016/j.desal.2010.12.010  
  257. Jabari F., Mohammadi-ivatloo B., Mohammadpourfard M. (2019). Robust optimal self-scheduling of potable water and power producers under uncertain electricity prices. Applied Thermal Engineering. Vol. 162, 114258. doi: https://doi.org/10.1016/j.applthermaleng.2019.114258  
  258. Sannino D., Sacco O., Chianese A. (2013). Determination of Optimal Operating Condition in Nanofiltration (NF) and Reverse Osmosis (RO) During the Treatment of a Tannery Wastewater Stream. Chemical Engineering Transactions. Vol. 32, pp. 1993-1998. doi: https://doi.org/10.3303/CET1332333  
  259. Zhao P., Bai Y., Liu B., Chang H., Cao Y., Fang J. (2019). Process optimization for producing ultrapure water with high resistivity and low total organic carbon. Process Safety and Environmental Protection. Vol. 126, pp. 232-241. doi: https://doi.org/10.1016/j.psep.2019.04.017  
  260. Namany S., Al-Ansari T., Govindan R. (2019). Optimisation of the energy, water, and food nexus for food security scenarios. Computers & Chemical Engineering. Vol. 129, 106513. doi: https://doi.org/10.1016/j.compchemeng.2019.106513  
  261. Cao Zh., Deng J., Ye F., Garris Ch. A. (2017). Performance Analysis of Thermal Vapor Compression Integrated with Reverse Osmosis Desalination System. Chemical Engineering Transactions. Vol. 61, pp. 919-924. doi: https://doi.org/10.3303/CET1761151  
  262. Heidary B., Tavakoli Hashjin T., Ghobadian B., Roshande R. (2019). Performance analysis of hybrid solar-wind RO-MSF desalination system. Resource-Efficient Technologies. Vol. 2, pp. 1-16. doi: https://doi.org/10.18799/24056537/2019/2/184  
  263. Heidary B., Tavakoli Hashjin T., Ghobadian B., Roshandel R. (2019). Exergy of a hybrid solar-wind reverse osmosis-MSF desalination system. Resource-Efficient Technologies. Vol. 1, pp. 8-19. doi: https://doi.org/10.18799/24056537/2019/1/227  
  264. Lacroix C., Perier-Muzet M., Stitou D. (2019). Dynamic Modeling and Preliminary Performance Analysis of a New Solar Thermal Reverse Osmosis Desalination Process. Energies. Vol. 12, Is. 20, 4015. doi: https://doi.org/10.3390/en12204015  
  265. Haryati S., Hamzah A. B., Goh P. S., Abdullah M. S., Ismail A. F., Bustan M. D. (2017). Process intensification of seawater reverse osmosis through enhanced train capacity and module size – Simulation on Lanzarote IV SWRO plant. Desalination. Vol. 408, pp. 92-101. doi: https://doi.org/10.1016/j.desal.2017.01.011  
  266. Ling Ch., Wang Y., Min Ch., Zhang Y. (2018). Economic evaluation of reverse osmosis desalination system coupled with tidal energy. Frontiers in Energy. Vol. 12, pp. 297–304. doi: https://doi.org/10.1007/s11708-017-0478-2  
  267. García Latorre F. J., Pérez Báez S. O., Gómez Gotor A. (2015). Energy performance of a reverse osmosis desalination plant operating with variable pressure and flow. Desalination. Vol. 366, pp. 146-153. doi: https://doi.org/10.1016/j.desal.2015.02.039  
  268. Nayar K. G., Fernandes J., McGovern R. K., Dominguez K. P., McCance A., Al-Anzi B. S., Lienhard V J. H. (2019). Cost and energy requirements of hybrid RO and ED brine concentration systems for salt production. Desalination. Vol. 456, pp. 97-120. doi: https://doi.org/10.1016/j.desal.2018.11.018  
  269. Qin M., Deshmukh A., Epsztein R., Patel S. K., Owoseni O. M., Walker W. Sh., Elimelech M. (2019). Comparison of energy consumption in desalination by capacitive deionization and reverse osmosis. Desalination. Vol. 455, pp. 100-114. doi: https://doi.org/10.1016/j.desal.2019.01.003  
  270. . Nayar K. G., Fernandes J., McGovern R. K., Al-Anzi B. S., Lienhard V J. H. (2019). Cost and energy needs of RO-ED-crystallizer systems for zero brine discharge seawater desalination. Desalination. Vol. 457, pp. 115-132. doi: https://doi.org/10.1016/j.desal.2019.01.015  
  271. Koutsou C.P., Kritikos E., Karabelas A.J., Kostoglou M. (2020). Analysis of temperature effects on the specific energy consumption in reverse osmosis desalination processes. Desalination. Vol. 476, 114213. doi: https://doi.org/10.1016/j.desal.2019.114213  
  272. Delgado-Torres A. M., García-Rodríguez L., del Moral M. J. (2020). Preliminary assessment of innovative seawater reverse osmosis (SWRO) desalination powered by a hybrid solar photovoltaic (PV) – Tidal range energy system. Desalination. Vol. 477, 114247. doi: https://doi.org/10.1016/j.desal.2019.114247  
  273. . Arsović M. R., Topić R. M., Komatina M. S., Gojak M. (2015). Thermodynamical research of using solar energy for desalination of seawater. Thermal Science. Vol. 19, No. 5, pp. 1709-1721. doi: https://doi.org/10.2298/TSCI141220074A  
  274. Reimers A. S., Webber M. E. (2018). Systems-level thermodynamic and economic analysis of a seawater reverse osmosis desalination plant integrated with a combined cycle power plant. Texas Water Journal. Vol. 9, No 1, pp. 82-95. doi: https://doi.org/10.21423/twj.v9i1.7065  
  275. Akhatov J. S. (2016). Energy and Exergy Analysis of Solar PV Powered Reverse Osmosis Desalination. Applied Solar Energy. Vol. 52, pp. 265–270. doi: https://doi.org/10.3103/S0003701X16040034  
  276. Alanezi A. A., Altaee A., Sharif A. O. (2020). The effect of energy recovery device and feed flow rate on the energy efficiency of reverse osmosis process. Chemical Engineering Research and Design. Vol. 158, pp. 12-23. doi: https://doi.org/10.1016/j.cherd.2020.03.018  
  277. Bartholomew T. V., Mey L., Arena J. T., Siefert N. S., Mauter M. S. (2017). Osmotically assisted reverse osmosis for high salinity brine treatment. Desalination. Vol. 421, pp. 3-11. doi: https://doi.org/10.1016/j.desal.2017.04.012  
  278. Chae S. H., Seo J., Kim J., Kim Y. M., Kim J. H. (2018). A simulation study with a new performance index for pressure-retarded osmosis processes hybridized with seawater reverse osmosis and membrane distillation. Desalination. Vol. 444, pp. 118-128. doi: https://doi.org/10.1016/j.desal.2018.07.019  
  279. El-Sayed T. A., Abdel Fatah A. A. (2016). Performance of hydraulic turbocharger integrated with hydraulic energy management in SWRO desalination plants. Desalination. Vol. 379, pp. 85-92. doi: https://doi.org/10.1016/j.desal.2015.10.013  
  280. Jia X., Klemeš J. J., Varbanov P. S., Alwi Sh. R. W. (2019). Analyzing the Energy Consumption, GHG Emission, and Cost of Seawater Desalination in China. Energies. Vol. 12, Is. 3, 463. doi: https://doi.org/10.3390/en12030463  
  281.  Castro M., Alcanzare M., Esparcia Jr. E., Oco J. (2020). A Comparative Techno-Economic Analysis of Different Desalination Technologies in Off-Grid Islands. Energies. Vol. 13, Is. 9., 2261. doi: https://doi.org/10.3390/en13092261  
  282. Karabelas A.J., Koutsou C.P., Kostoglou M., Sioutopoulos D.C. (2018). Analysis of specific energy consumption in reverse osmosis desalination processes. Desalination. Vol. 431, pp. 15-21. doi: https://doi.org/10.1016/j.desal.2017.04.006  
  283. Mazlan N. M., Peshev D., Livingston A. G. (2016). Energy consumption for desalination — A comparison of forward osmosis with reverse osmosis, and the potential for perfect membranes. Desalination. Vol. 377, pp. 138-151. doi: https://doi.org/10.1016/j.desal.2015.08.011  
  284. Minhas M.B., Jande Y.A.C., Kim W.S. (2014). Combined reverse osmosis and constant-current operated capacitive deionization system for seawater desalination. Desalination. Vol. 344, pp. 299-305. doi: https://doi.org/10.1016/j.desal.2014.03.043  
  285. Segal H., Birnhack L., Nir O., Lahav O. (2018). Intensification and energy minimization of seawater reverse osmosis desalination through high-pH operation: Temperature dependency and second pass implications. Chemical Engineering and Processing – Process Intensification. Vol. 131, pp. 84-91. doi: https://doi.org/10.1016/j.cep.2018.07.009  
  286. Lourenço A. B., Carvalho M. (2020). Exergoeconomic and exergoenvironmental analyses of an off-grid reverse osmosis system with internal combustion engine and waste heat recovery. Chemical Engineering Journal Advances. Vol. 4, 100056. doi:  https://doi.org/10.1016/j.ceja.2020.100056  
  287. Muhammad A. J., Qureshi B. A., Zubair S. M. (2017). Exergo-economic analysis of a seawater reverse osmosis desalination plant with various retrofit options. Desalination. Vol. 401, pp. 88-98. doi: https://doi.org/10.1016/j.desal.2016.09.032  
  288. Eshoul N. M., Agnew B., Al-Weshahi M. A., Atab M. S. (2015). Exergy Analysis of a Two-Pass Reverse Osmosis (RO) Desalination Unit with and without an Energy Recovery Turbine (ERT) and Pressure Exchanger (PX). Energies. Vol. 8, Is. 7, pp. 6910-6925. doi:  https://doi.org/10.3390/en8076910  
  289. Islam Sh., Dincer I., Yilbas B. S. (2018). Development of a novel solar-based integrated system for desalination with heat recovery. Applied Thermal Engineering. Vol. 129, pp. 1618-1633. doi: https://doi.org/10.1016/j.applthermaleng.2017.09.02 8
  290. Mokhtari H., Sepahvand M., Fasihfar A. (2016). Thermoeconomic and exergy analysis in using hybrid systems (GT + MED + RO) for desalination of brackish water in Persian Gulf. Desalination. Vol. 399, pp. 1-15. doi: https://doi.org/10.1016/j.desal.2016.07.044  
  291. Sadri S., Ameri M., Khoshkhoo R. H. (2017). Multi-objective optimization of MED-TVC-RO hybrid desalination system based on the irreversibility concept. Desalination. Vol. 402, pp. 97-108. doi: https://doi.org/10.1016/j.desal.2016.09.029  
  292. Li Q., Moya W., Esfahani I. J., Rashidi J., Yoo Ch. K. (2017). Integration of reverse osmosis desalination with hybrid renewable energy sources and battery storage using electricity supply and demand-driven power pinch analysis. Process Safety and Environmental Protection. Vol. 111, pp. 795-809. doi: https://doi.org/10.1016/j.psep.2017.09.009  
  293. Palenzuela P., Zaragoza G., Alarcón D., Blanco J. (2011). Simulation and evaluation of the coupling of desalination units to parabolic-trough solar power plants in the Mediterranean region. Desalination. Vol. 281, pp. 379-387. doi: https://doi.org/10.1016/j.desal.2011.08.014  
  294. Shrivastava A., Rosenberg S., Peery M. (2015). Energy efficiency breakdown of reverse osmosis and its implications on future innovation roadmap for desalination. Desalination. Vol. 368, pp. 181-192. doi: https://doi.org/10.1016/j.desal.2015.01.005  
  295. Mansour T. M., Ismail T. M., Ramzy Kh., El-Salam M. A. (2020). Energy recovery system in small reverse osmosis desalination plant: Experimental and theoretical investigations. Alexandria Engineering Journal. Vol. 59, Is. 5, pp. 3741-3753. doi: https://doi.org/10.1016/j.aej.2020.06.030  
  296. Mansouri M. T., Amidpour M., Ponce-Ortega J. M. (2019). Optimal integration of organic Rankine cycle and desalination systems with industrial processes: Energy-water-environment nexus. Applied Thermal Engineering. Vol. 158, 113740. doi: https://doi.org/10.1016/j.applthermaleng.2019.113740  
  297. Kaya A., Evren Tok M., Koc M. (2019). A Levelized Cost Analysis for Solar-Energy-Powered Sea Water Desalination in The Emirate of Abu Dhabi. Sustainability. Vol. 11, Is 6, 1691. doi: https://doi.org/10.3390/su11061691  
  298. Aydiner C., Sen U., Topcu S., Ekinci D., Altinay A. D., Koseoglu-Imer D. Y., Keskinler B. (2014). Techno-economic viability of innovative membrane systems in water and mass recovery from dairy wastewater. Journal of Membrane Science. Vol. 458, pp. 66-75. doi: https://doi.org/10.1016/j.memsci.2014.01.058  
  299. He W., Wang Y., Sharif A., Shaheed M. H. (2014). Thermodynamic analysis of a stand-alone reverse osmosis desalination system powered by pressure retarded osmosis. Desalination. Vol. 352, pp. 27-37. doi: https://doi.org/10.1016/j.desal.2014.08.006  
  300. Kim J. E., Phuntsho Sh., Chekli L., Choi J. Y., Shon H. K. (2018). Environmental and economic assessment of hybrid FO-RO/NF system with selected inorganic draw solutes for the treatment of mine impaired water. Desalination. Vol. 429, pp. 96-104. doi: https://doi.org/10.1016/j.desal.2017.12.016  
  301. Gökçek M. (2018). Integration of hybrid power (wind-photovoltaic-diesel-battery) and seawater reverse osmosis systems for small-scale desalination applications. Desalination. Vol. 435, pp. 210-220. doi: https://doi.org/10.1016/j.desal.2017.07.006  
  302. Caldera U., Bogdanov D., Breyer Ch. (2016). Local cost of seawater RO desalination based on solar PV and wind energy: A global estimate. Desalination. Vol. 385, pp. 207-216. doi: https://doi.org/10.1016/j.desal.2016.02.004  
  303. Clarke D. P., Al-Abdeli Y. M., Kothapalli G. (2013). The effects of including intricacies in the modelling of a small-scale solar-PV reverse osmosis desalination system. Desalination. Vol. 311, pp.127-136. doi: https://doi.org/10.1016/j.desal.2012.11.006  
  304. Ma Q., Lu H. (2011). Wind energy technologies integrated with desalination systems: Review and state-of-the-art. Desalination. Vol. 277, Is. 1–3, pp. 274-280. doi: https://doi.org/10.1016/j.desal.2011.04.041  
  305. Hirsimaki C., Outram J. G., Millar G. J., Altaee A. (2020). Process simulation of high pH reverse osmosis systems to facilitate reuse of coal seam gas associated water. Journal of Environmental Chemical Engineering. Vol. 8, Is. 5, 104122. doi: https://doi.org/10.1016/j.jece.2020.104122  
  306. Gökçek M., Gökçek Ö. B. (2016). Technical and economic evaluation of freshwater production from a wind-powered small-scale seawater reverse osmosis system (WP-SWRO). Desalination. Vol. 381, pp. 47-57. doi: https://doi.org/10.1016/j.desal.2015.12.004  
  307. Al-Obaidi M.A., Filippini G., Manenti F., Mujtaba I.M. (2019). Cost evaluation and optimisation of hybrid multi effect distillation and reverse osmosis system for seawater desalination. Desalination. Vol. 456, pp. 136-149. doi: https://doi.org/10.1016/j.desal.2019.01.019  
  308. Filippini G., Al-Obaidi M.A., Manenti F., Mujtaba I.M. (2019). Design and economic evaluation of solar-powered hybrid multi effect and reverse osmosis system for seawater desalination. Desalination. Vol. 465, pp.114-125. doi: https://doi.org/10.1016/j.desal.2019.04.016  
  309. Im S. J., Jeong S., Jeong S., Jang A. (2020). Techno-economic evaluation of an element-scale forward osmosis-reverse osmosis hybrid process for seawater desalination. Desalination. Vol. 476, 114240. doi: https://doi.org/10.1016/j.desal.2019.114240  
  310. Tobin T., Gustafson R., Bura R., Gough H. L. (2020). Integration of wastewater treatment into process design of lignocellulosic biorefineries for improved economic viability. Biotechnology for Biofuels. Vol. 13, 24. doi: https://doi.org/10.1186/s13068-020-1657-7  
  311. Blandin G., Verliefde A. R.D., Tang Ch. Y., Le-Clech P. (2015). Opportunities to reach economic sustainability in forward osmosis–reverse osmosis hybrids for seawater desalination. Desalination. Vol. 363, pp. 26-36. doi: https://doi.org/10.1016/j.desal.2014.12.011  
  312. Castel Ch., Favre E. (2018). Membrane separations and energy efficiency. Journal of Membrane Science. Vol. 548, pp. 345-357. doi: https://doi.org/10.1016/j.memsci.2017.11.035  
  313. Kook S., Lee Ch., Nguyen Th. T., Lee J., Shon H. K., Kim I. S. (2018). Serially connected forward osmosis membrane elements of pressure-assisted forward osmosis-reverse osmosis hybrid system: Process performance and economic analysis. Desalination. Vol. 448, pp. 1-12. doi: https://doi.org/10.1016/j.desal.2018.09.019  
  314. Loutatidou S., Arafat H. A. (2015). Techno-economic analysis of MED and RO desalination powered by low-enthalpy geothermal energy. Desalination. Vol. 365, pp. 277-292. doi: https://doi.org/10.1016/j.desal.2015.03.010  
  315. Valizadeh B., Ashtiani F. Z., Fouladitajar A., Dabir B., Baraghani S. S. M.б Armand S. B., Salari B., Kouchakiniya N. (2015). Scale-up economic assessment and experimental analysis of MF–RO integrated membrane systems in oily wastewater treatment plants for reuse application. Desalination. Vol. 374, pp. 31-37. doi: https://doi.org/10.1016/j.desal.2015.07.017  
  316. Bick A., Gillerman L., Manor Y., Oron G. (2012). Economic Assessment of an Integrated Membrane System for Secondary Effluent Polishing for Unrestricted Reuse. Water. Vol. 4, Is. 1, pp. 219-236. doi: https://doi.org/10.3390/w4010219  
  317. Edalat A., Hoek E. M. V. (2020). Techno-Economic Analysis of RO Desalination of Produced Water for Beneficial Reuse in California. Water. Vol. 12, Is. 7, 1850. doi: https://doi.org/10.3390/w12071850  
  318. Toh K.Y., Liang Y.Y., Lau W.J., Fimbres Weihs G.A. (2020). The techno-economic case for coupling advanced spacers to high-permeance RO membranes for desalination. Desalination. Vol. 491, 114534. doi: https://doi.org/10.1016/j.desal.2020.114534  
  319. La Cerva M., Gurreri L., Cipollina A., Tamburini A., Ciofalo M., Micale G. (2019). Modelling and cost analysis of hybrid systems for seawater desalination: Electromembrane pre-treatments for Reverse Osmosis. Desalination. Vol. 467, pp. 175-195. doi: https://doi.org/10.1016/j.desal.2019.06.010  
  320. Ghafoor A., Ahmed T., Munir A., Arslan Ch., Ahmad S.A. (2020). Techno-economic feasibility of solar based desalination through reverse osmosis. Desalination. Vol. 485, 114464. doi: https://doi.org/10.1016/j.desal.2020.114464  
  321. Castro M. T., Esparcia Jr. E. A., Odulio C. M. F., Ocon J. D. (2019). Technoeconomics of Reverse Osmosis as Demand-Side Management for Philippine Off-Grid Islands. Chemical Engineering Transactions. Vol. 76, pp. 1129-1134. doi: https://doi.org/10.3303/CET1976189  
  322. Widiasa I.N., Yoshi L.A. (2016). Techno-Economy Analysis A Small Scale Reverse Osmosis System for Brackish Water Desalination. International Journal of Science and Engineering. Vol. 10, Is. 2, pp. 51-57. doi: https://doi.org/10.12777/ijse.10.2.51-57  
  323. Hoveidi H., Vahidi H., CheraghAli S. M. T., Aslemanda A. (2017). Economic Evaluation of RO and MEH Desalination Units in Iranian South-Eastern Villages. Vol. 1, Is. 1., pp. 99-112. doi: https://doi.org/10.22097/EEER.2017.46460  
  324. Laissaoui M., Palenzuela P., Sharaf Eldean M. A., Nehari D., Alarcón-Padilla D.-C. (2018). Techno-economic analysis of a stand-alone solar desalination plant at variable load conditions. Applied Thermal Engineering. Vol. 133, pp. 659-670. doi: https://doi.org/10.1016/j.applthermaleng.2018.01.074  
  325. Abejon R., Abejon A., Puthai W., Ibrahim S.B., Nagasawa H., Tsuru T., Garea A. (2017). Preliminary techno-economic analysis of non-commercial ceramic and organosilica membranes for hydrogen peroxide ultrapurification. Chemical Engineering Research and Design. Vol. 125, pp. 385-397. doi: https://doi.org/10.1016/j.cherd.2017.07.018  
  326. Kumar Sh., Groth A., Vlacic L. (2014). An analytical index for evaluating manufacturing cost and performance of low-pressure hollow fibre membrane systems. Desalination. Vol. 332, Is. 1, pp. 44-51. doi: https://doi.org/10.1016/j.desal.2013.10.013  
  327. Ochando-Pulido J. M., Hodaifa G., Victor-Ortega M. D., Martinez-Ferez A. (2013). Performance Modeling and Cost Analysis of a Pilot-Scale Reverse Osmosis Process for the Final Purification of Olive Mill Wastewater. Membranes. Vol. 3, Is. 4, pp. 285-297. doi: https://doi.org/10.3390/membranes3040285  
  328. Abraham T., Luthra A. (2011). Socio-economic & technical assessment of photovoltaic powered membrane desalination processes for India. Desalination. Vol. 268, Is. 1–3, pp. 238-248. doi: https://doi.org/10.1016/j.desal.2010.10.035  
  329. Idrees M. F. (2020). Performance Analysis and Treatment Technologies of Reverse Osmosis Plant – A case study. Case Studies in Chemical and Environmental Engineering. Vol. 2, 100007. doi: https://doi.org/10.1016/j.cscee.2020.100007  
  330. Al-Obaidi M.A., Jarullah A.T., Kara-Zaїtri C., Mujtaba I.M. (2018). Simulation of hybrid trickle bed reactor–reverse osmosis process for the removal of phenol from wastewater. Computers & Chemical Engineering. Vol. 113, pp. 264-273. doi: https://doi.org/10.1016/j.compchemeng.2018.03.016  
  331. Al-Obaidi M.A., Kara-Zaïtri C., Mujtaba I.M. (2019). Evaluation of chlorophenol removal from wastewater using multi-stage spiral-wound reverse osmosis process via simulation. Computers & Chemical Engineering. Vol. 130, 106522. doi: https://doi.org/10.1016/j.compchemeng.2019.106522  
  332. Alsarayreh A. A., Al-Obaidi M.A., Al-Hroub A.M., Patel R., Mujtaba I.M. (2020). Performance evaluation of reverse osmosis brackish water desalination plant with different recycled ratios of retentate. Computers & Chemical Engineering. Vol. 135, 106729. doi: https://doi.org/10.1016/j.compchemeng.2020.106729  
  333. Riverol C., Pilipovik M.V. (2011). Prediction of the behaviour of the Silt Density Index (SDI) in the Caribbean Seawater and its impact on RO desalination plants. Desalination. Vol. 268, Is. 1–3, pp. 262-265. doi: https://doi.org/10.1016/j.desal.2010.09.049
  334. Venkatesan A. K., Ahmad S., Johnson W., Batista J. R. (2011). Salinity reduction and energy conservation in direct and indirect potable water reuse. Desalination. Vol. 272, Is. 1–3, pp. 120-127. doi: https://doi.org/10.1016/j.desal.2011.01.007
  335. Zhou J., Chang V. W.-C., Fane A. G. (2011). Environmental life cycle assessment of reverse osmosis desalination: The influence of different life cycle impact assessment methods on the characterization results. Desalination. Vol. 283, pp. 227-236. doi: https://doi.org/10.1016/j.desal.2011.04.066  
  336. Pascual X., Gu H., Bartman A. R., Zhu A., Rahardianto A., Giralt J., Rallo R., Christofides P. D., Cohen Y. (2013). Data-driven models of steady state and transient operations of spiral-wound RO plant. Desalination. Vol. 316, pp. 154-161. doi: https://doi.org/10.1016/j.desal.2013.02.006  
  337. Qian Z., Miedema H., de Smet L.C.P.M., Sudholter E.J.R. (2018). Modelling the selective removal of sodium ions from greenhouse irrigation water using membrane technology. Chemical Engineering Research and Design. Vol. 134, pp. 154-161, doi: https://doi.org/10.1016/j.cherd.2018.03.040  
  338. Phuc B. D. H., You S.-S., Lim T.-W., Kim H.-S. (2017). Dynamical analysis and control synthesis of RO desalination process against water hammering. Desalination. Vol. 402, pp. 133-142. doi: https://doi.org/10.1016/j.desal.2016.09.023  
  339. Cao Zh., Deng J., Ye F., Garris Jr. Ch. A. (2018). Analysis of a hybrid Thermal Vapor Compression and Reverse Osmosis desalination system at variable design conditions. Desalination. Vol. 438, pp. 54-62, doi: https://doi.org/10.1016/j.desal.2018.03.019  
  340. Lu Y., Liao A., Hu Y. (2013) Design of reverse osmosis networks for multiple freshwater production. Korean Journal of Chemical Engineering. Vol, 30, pp. 988–996. doi: https://doi.org/10.1007/s11814-013-0009-8  
  341. Qian Zh, Liu X., Yu Zh., Zhang H., Jü Y. (2012). A Pilot-scale Demonstration of Reverse Osmosis Unit for Treatment of Coal-bed Methane Co-produced Water and Its Modeling. Chinese Journal of Chemical Engineering. Vol. 20, Is. 2, pp. 302-311. doi: https://doi.org/10.1016/S1004-9541(12)60392-9  
  342. Salo, A. (2017). Simulation of water purification machine for vending cyber physical systems. Technology Audit and Production Reserves, Vol. 2, No. (2(40), pp. 16–21. doi: https://doi.org/10.15587/2312-8372.2018.128543  
  343. Lucay F., Cisternas L.A., Gálvez E.D. (2015). Global sensitivity analysis for identifying critical process design decisions. Chemical Engineering Research and Design. Vol. 103, pp. 74-83. doi: https://doi.org/10.1016/j.cherd.2015.06.015  
  344. Singh S., Henderson R. K., Baker A., Stuetz R. M., Khan S. J. (2012). Characterisation of reverse osmosis permeates from municipal recycled water systems using fluorescence spectroscopy: Implications for integrity monitoring. Journal of Membrane Science. Vol. 421–422, pp. 180-189. doi: https://doi.org/10.1016/j.memsci.2012.07.008  
  345. Kim Y. Ch., Min T. (2020). Influence of osmotic mediation on permeation of water in reverse osmosis: Experimental and numerical analysis. Journal of Membrane Science. Vol. 595, 117574. doi: https://doi.org/10.1016/j.memsci.2019.117574  
  346. Huang Q., Ma W. (2012). A model of estimating scaling potential in reverse osmosis and nanofiltration systems. Desalination. Vol. 288, pp. 40-46. doi: https://doi.org/10.1016/j.desal.2011.12.007  
  347. Kim D.Y., Gu B., Yang D.R. (2013). An explicit solution of the mathematical model for osmotic desalination process. Korean Journal of Chemical Engineering. Vol. 30, pp. 1691–1699. doi: https://doi.org/10.1007/s11814-013-0123-7  
  348. Raim V., Srebnik S. (2018). Simulation of osmotic pressure across an amorphous semipermeable membrane. Journal of Membrane Science. Vol. 563, pp. 183-190. doi: https://doi.org/10.1016/j.memsci.2018.05.058  
  349. Ochando-Pulido J. M., Martínez-Férez A., Stoller M. (2019). Analysis of the Flux Performance of Different RO/NF Membranes in the Treatment of Agroindustrial Wastewater by Means of the Boundary Flux Theory. Membranes. Vol. 9, Is. 1, 2. doi: https://doi.org/10.3390/membranes9010002  
  350. Rivas-Perez R., Sotomayor-Moriano J., Pérez-Zuñiga G., Soto-Angles M. E. (2019). Real-Time Implementation of an Expert Model Predictive Controller in a Pilot-Scale Reverse Osmosis Plant for Brackish and Seawater Desalination. Applied Sciences. Vol. 9, Is. 14, 2932. doi: https://doi.org/10.3390/app9142932  
  351. Manheim D. C., Jiang S. C. (2017). Investigation of Algal Biotoxin Removal during SWRO Desalination through a Materials Flow Analysis. Water. Vol. 9, Is. 10, 730. doi:  https://doi.org/10.3390/w9100730  
  352. Zafra-Cabeza A., Ridao M. A., Camacho E. F. (2011). A mixed integer quadratic programming formulation of risk management for reverse osmosis plants. Desalination. Vol. 268, Is. 1–3, pp. 46-54. doi: https://doi.org/10.1016/j.desal.2010.09.048  
  353. Bourouni K. (2013). Availability assessment of a reverse osmosis plant: Comparison between Reliability Block Diagram and Fault Tree Analysis Methods. Desalination. Vol. 313, pp. 66-76. doi: https://doi.org/10.1016/j.desal.2012.11.025  
  354. Ramon G. Z., Hoek E. M.V. (2013). Transport through composite membranes, part 2: Impacts of roughness on permeability and fouling. Journal of Membrane Science. Vol. 425–426, pp. 141-148. doi: https://doi.org/10.1016/j.memsci.2012.08.004  
  355. Waly T., Kennedy M. D., Witkamp G.-J., Amy G., Schippers J. C. (2011). Predicting and measurement of pH of seawater reverse osmosis concentrates. Desalination. Vol. 280, Is. 1–3, pp.27-32. doi: https://doi.org/10.1016/j.desal.2011.06.057  
  356. Alhseinat E., Sheikholeslami R. (2012). A completely theoretical approach for assessing fouling propensity along a full-scale reverse osmosis process.  Desalination. Vol. 301, pp. 1-9. doi: https://doi.org/10.1016/j.desal.2011.12.014  
  357. Lee B.-S. (2015). Nuclide separation modeling through reverse osmosis membranes in radioactive liquid waste. Nuclear Engineering and Technology. Vol. 47, Is. 7, pp. 859-866. doi: https://doi.org/10.1016/j.net.2015.08.001  
  358. Kezia K., Lee J., Ogieglo W., Hill A., Benes N. E., Kentish S. E. (2014). The transport of hydronium and hydroxide ions through reverse osmosis membranes. Journal of Membrane Science. Vol. 459, pp. 197-206. doi: https://doi.org/10.1016/j.memsci.2014.02.018  
  359. Karakhim S. O., Zhuk P. F., Kosterin S. O. (2020). Kinetics simulation of transmembrane transport of ions and molecules through a semipermeable membrane. Journal of Bioenergetics and Biomembranes. Vol. 52, pp. 47–60. doi: https://doi.org/10.1007/s10863-019-09821-8  
  360. Peñate B., García-Rodríguez L. (2012). Seawater reverse osmosis desalination driven by a solar Organic Rankine Cycle: Design and technology assessment for medium capacity range. Desalination. Vol. 284, pp. 86-91. doi: https://doi.org/10.1016/j.desal.2011.08.040
  361. Aghababaei N. (2017). Reverse osmosis design with IMS design software to produce drinking water in Bandar Abbas, Iran. Journal of Applied Research in Water and Wastewater. Vol. 7, Is. 1, pp. 314-318. doi: https://doi.org/10.22126/ARWW.2017.776  
  362.  Park K., Burlace L., Dhakal N., Mudgal A., Stewartd N. A., Davies P. A. (2020). Design, modelling and optimisation of a batch reverse osmosis (RO) desalination system using a free piston for brackish water treatment. Desalination. Vol. 494, 114625. doi: https://doi.org/10.1016/j.desal.2020.114625  
  363. Al-hotmani O. M. A., Al-Obaidi M. A. A., John Y. M., Patel R., Mujtaba I. M. (2020). An Innovative Design of an Integrated MED-TVC and Reverse Osmosis System for Seawater Desalination: Process Explanation and Performance Evaluation. Processes. Vol. 8, Is. 5, 607. doi:  https://doi.org/10.3390/pr8050607  
  364. Srivastava S., Vaddadi S., Kumar P., Sadistap Sh. (2018). Design and development of reverse osmosis (RO) plant status monitoring system for early fault prediction and predictive maintenance. Applied Water Science. Vol. 8, 159. doi: https://doi.org/10.1007/s13201-018-0821-8  
  365. Husnil Y. A., Harvianto G. R., Andika R., Chaniago Y. D., Lee M. (2017). Conceptual designs of integrated process for simultaneous production of potable water, electricity, and salt. Desalination. Vol. 409, pp. 96-107. doi: https://doi.org/10.1016/j.desal.2017.01.024

Full Text



© 2014-2022 Sumy State University
"Journal of Engineering Sciences"
ISSN 2312-2498 (Print), ISSN 2414-9381 (Online).
All rights are reserved by SumDU