References

  1. https://www.waterworld.com/international/wastewater/article/16201682/analysis-global-water-stress-by-2040, Accessed on 20th June 2021.
  2. R. Al Hashemi, S. Zarreen, A. Al Raisi, F.A. Al Marzooqi, S.W. Hasan, A review of desalination trends in Gulf Cooperation Council Countries, Int. J. Sci. Res., 2 (2014) 72–96.
  3. https://www.iea.org/commentaries/desalinated-water-affectsthe-energy-equation-in-the-middle-east, Accessed on 20th June 2021.
  4. M.A. Darwish, H.K. Abdulrahim, A.S. Hassan, A.A. Mabrouk, PV and CSP solar technologies and desalination: an economic analysis, Desal. Water Treat., 57 (2016) 16679–16702.
  5. M. Sadi, H. Fakharian, H. Ganji, M. Kakavand, Evolving artificial intelligence techniques to model the hydrate-based desalination process of produced water, J. Water Reuse Desal., 9 (2019) 372–384.
  6. S. AlZu’bi, M. Alsmirat, M. Al-Ayyoub, Y. Jararweh, Artificial Intelligence Enabling Water Desalination Sustainability Optimization, 2019 7th International Renewable and Sustainable Energy Conference (IRSEC), IEEE, Agadir, Morocco, 2019, pp. 1–4.
  7. N. Parveen, S. Zaidi, M. Danish, Artificial intelligence (AI)-based reverse osmosis water desalination models, IWRA (India) J., 8 (2019) 44–50.
  8. Y. Wang, Z. Cao, A. Barati Farimani, Ozark nanopore: highly efficient and selective graphene nanopore designed by artificial intelligence for water desalination, Bull. Am. Phys. Soc., (2020), 2020APS.DFDJ16004W.
  9. S. Vrkalovic, E.-C. Lunca, I.-D. Borlea, Model-free sliding mode and fuzzy controllers for reverse osmosis desalination plants, Int. J. Artif. Intell., 16 (2018) 208–222.
  10. M. Ehteram, S.Q. Salih, Z.M. Yaseen, Efficiency evaluation of reverse osmosis desalination plant using hybridized multilayer perceptron with particle swarm optimization, Environ. Sci. Pollut. Res., 27 (2020) 15278–15291.
  11. H.M. El-Arwash, A.M. Azmy, E.M. Rashad, A GA-Based Initialization of PSO for Optimal APFS Allocation in Water Desalination Plant, 2017 Nineteenth International Middle East Power Systems Conference (MEPCON), IEEE, Cairo, Egypt, 2017, pp. 1378–1384.
  12. N.S. Rathore, V.P. Singh, Whale optimization algorithm-based controller design for reverse osmosis desalination plants, Int. J. Intell. Eng., 7 (2019) 77–88.
  13. R. Rustum, A.M.J. Kurichiyanil, S. Forrest, C. Sommariva, A.J. Adeloye, M. Zounemat-Kermani, M. Scholz, Sustainability ranking of desalination plants using mamdani fuzzy logic inference systems, Sustainability, 12 (2020) 631, doi: 10.3390/ su12020631.
  14. P. Kofinas, A.I. Dounis, Online tuning of a PID controller with a fuzzy reinforcement learning MAS for flow rate control of a desalination unit, Electronics, 8 (2019) 231, doi: 10.3390/electronics8020231.
  15. M.T. Gaudio, G. Coppola, L. Zangari, S. Curcio, S. Greco, S. Chakraborty, Artificial intelligence-based optimization of industrial membrane processes, Earth Syst. Environ., 5 (2021) 385–398.
  16. Y. Choi, Y. Lee, K. Shin, Y. Park, S. Lee, Analysis of long-term performance of full-scale reverse osmosis desalination plant using artificial neural network and tree model, Environ. Eng. Res., 25 (2020) 763–770.
  17. A.V. Dudchenko, M.S. Mauter, Neural networks for estimating physical parameters in membrane distillation, J. Membr. Sci., 610 (2020) 118285, doi: 10.1016/j.memsci.2020.118285.
  18. P. Gao, L. Zhang, K. Cheng, H. Zhang, A new approach to performance analysis of a seawater desalination system by an artificial neural network, Desalination, 205 (2007) 147–155.
  19. M. Faegh, P. Behnam, M.B. Shafii, M. Khiadani, Development of artificial neural networks for performance prediction of a heat pump assisted humidification-dehumidification desalination system, Desalination, 508 (2021) 115052.
  20. K.A. Al-Shayji, S. Al-Wadyei, A. Elkamel, Modelling and optimization of a multistage flash desalination process, Eng. Optim., 37 (2005) 591–607.
  21. M. Barello, D. Manca, R. Patel, I.M. Mujtaba, Neural network based correlation for estimating water permeability constant in RO desalination process under fouling, Desalination, 345 (2014) 101–111.
  22. M. Derbali, S.M. Buhari, G. Tsaramirsis, M. Stojmenovic, H. Jerbi, M.N. Abdelkrim, M.H. Al-Beirutty, Water desalination fault detection using machine learning approaches: a comparative study, IEEE Access, 5 (2017) 23266–23275.
  23. M.E. El-Hawary, Artificial neural networks and possible applications to desalination, Desalination, 92 (1993) 125–147.
  24. G.P. Rao, D.M.K. Al-Gobaisi, A. Hassan, A. Kurdali, R. Borsani, M. Aziz, Towards improved automation for desalination processes, Part II: intelligent control, Desalination, 8 (1994) 507–528.
  25. K.A. Al-Shayji, Y.A. Liu, Predictive modeling of large-scale commercial water desalination plants: data-based neural network and model-based process simulation, Ind. Eng. Chem. Res., 41 (2002) 6460–6474.
  26. R. Selvaraj, P.B. Deshpande, S.S. Tambe, B.D. Kulkami, Neural networks for the identification of MSF desalination plants, Desalination, 101 (1995) 185–193.
  27. A. Aminian, Prediction of temperature elevation for seawater in multi-stage flash desalination plants using radial basis function neural networks, Chem. Eng. J., 162 (2010) 552–556.
  28. H.R. Godini, M. Ghadrdan, M.R. Omidkhah, S.S. Madaeni, Part II: prediction of the dialysis process performance using artificial neural network (ANN), Desalination, 265 (2011) 11–21.
  29. W. Cao, Q. Liu, Y. Wang, I.M. Mujtaba, Modeling and simulation of VMD desalination process by ANN, Comput. Chem. Eng., 84 (2016) 96–103.
  30. M.M. Jafar, A. Zilouchian, Prediction of critical desalination parameters using radial basis functions networks, J. Intell. Rob. Syst., 34 (2002) 219–230.
  31. Z.V.P. Murthy, M.M. Vora, Prediction of reverse osmosis performance using artificial neural network, Indian J. Chem. Technol., 11 (2004) 108–115.
  32. A. Abbas, N. Al-Bastaki, Modeling of an RO water desalination unit using neural networks, Chem. Eng. J., 114 (2005) 139–143.
  33. Y.G. Lee, Y.S. Lee, J.J. Jeon, S. Lee, D.R. Yang, I.S. Kim, J.H. Kim, Artificial neural network model for optimizing operation of a seawater reverse osmosis desalination plant, Desalination, 247 (2009) 180–189.
  34. D. Libotean, J. Giralt, F. Giralt, R. Rallo, T. Wolfe, Y. Cohen, Neural network approach for modeling the performance of reverse osmosis membrane desalting, J. Membr. Sci., 326 (2009) 408–419.
  35. S.S. Madaeni, M. Shiri, A.R. Kurdian, Modeling, optimization, and control of reverse osmosis water treatment in Kazeroon power plant using neural network, Chem. Eng. Commun., 202 (2015) 6–14.
  36. A.M. Aish, H.A. Zaqoot, S.M. Abdeljawad, Artificial neural network approach for predicting reverse osmosis desalination plants performance in the Gaza Strip, Desalination, 367 (2015) 240–247.
  37. M. Barello, D. Manca, R. Patel, I.M. Mujtaba, Neural network based correlation for estimating water permeability constant in RO desalination process under fouling, Desalination, 345 (2014) 101–111.
  38. E.A. Roehl, D.A. Ladner, R.C. Daamen, J.B. Cook, J. Safarik, D.W. Phipps, P. Xie, Modeling fouling in a large RO system with artificial neural networks, J. Membr. Sci., 552 (2018) 95–106.