References

  1. 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.
  2. R. Segurado, J.F.A. Madeira, M. Costa, N. Duić, M.G. Carvalho, Optimization of a wind powered desalination and pumped hydro storage system, Appl. Energy, 177 (2016) 487–499.
  3. J.R. Werber, A. Deshmukh, M. Elimelech, Can batch or semibatch processes save energy in reverse-osmosis desalination? Desalination, 402 (2017) 109–122.
  4. E. Dimitriou, E.S. Mohamed, C. Karavas, G. Papadakis, Experimental comparison of the performance of two reverse osmosis desalination units equipped with different energy recovery devices, Desal. Wat. Treat., 55 (2015) 3019–3026.
  5. E. Dimitriou, E.S. Mohamed, G. Kyriakarakos, G. Papadakis, Experimental investigation of the performance of a reverse osmosis desalination unit under full- and part-load operation, Desal. Wat. Treat., 53 (2014) 3170–3178.
  6. S. Sobana, R.C. Panda, Development of a transient model for the desalination of sea/brackish water through reverse osmosis, Desal. Wat. Treat., 51 (2013) 2755–2767.
  7. K.A. Al-shayji, S. Al-wadyei, A. Elkamel, Modelling and optimization of a multistage flash desalination process, Eng. Optim., 37 (2005) 591–607.
  8. M. Jafar, A. Zilouchian, Application of Soft Computing for Desalination Technology, Intelligent Control Systems Using Soft Computing Methodologies, CRC Press, Boca Raton, FL, USA, 2001.
  9. K. Zhani, H. Ben Bacha, Modeling and simulation of a new design of the SMCEC desalination unit using solar energy, Desal. Wat. Treat., 21 (2010) 346–356.
  10. D. Libotean, Modeling the Reverse Osmosis Processes Performance Using Artificial Neural Networks, PhD Dissertation, Department of Chemical Engineering, Rovira I Virgili University, Tarragona, 2007.
  11. R.W.F. He, D. Han, C. Yue, W.H. Pu, A parametric study of a humidification dehumidification (HDH) desalination system using low grade heat sources, Energy Convers. Manage., 105 (2015) 929–937.
  12. L. Perkovic, T. Novosel, T. Pukšec, B. Cosic, M. Mustafa, G. Krajacic, N. Duica, Modeling of optimal energy flows for systems with close integration of sea water desalination and renewable energy sources: case study for Jordan, Energy Convers. Manage., 110 (2016) 249–259.
  13. H.-J. Oh, T.-M. Hwang, S. Lee, A simplified simulation model of RO systems for seawater desalination, Desalination, 238 (2009) 128–139.
  14. F.N. Alasfour, H.K. Abdulrahim, Rigorous steady state modeling of MSF-BR desalination system, Desal. Wat. Treat., 1 (2009) 259–276.
  15. J.S.R. Jang, ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, 1993, pp. 665–685.
  16. J.S.R. Jang, C.-T. Sun, Neuro-Fuzzy Modeling and Control, Proc. IEEE, 83 (1995) 378–406.
  17. M. Inal, Determination of dielectric properties of insulator materials by means of ANFIS: a comparative study, J. Mater. Process. Technol., 195 (2008) 34–43.
  18. R. Singh, A. Kainthola, T.N. Singh, Estimation of elastic of rocks using an ANFIS approach, Appl. Soft Comput., 12 (2012) 40–45.
  19. A. Khajeh, H. Modarress, B. Rezaee, Application of an adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers, Expert Syst. Appl., 36 (2009) 5728–5732.
  20. A. Melit, Artificial Intelligence-Based Modeling for Sizing of a Stand-Alone Photovoltaic Power System: Proposition for a New Model Using Neuro-Fuzzy Systems (ANFIS), 3rd International IEEE Conference on Intelligence Systems, London, UK, 2006, pp. 606–611.
  21. A.I. Dounis, G. Leftheriotis, S. Stavrinidis, G. Syrrokostas, Electrochromic device modeling using an adaptive neuro-fuzzy inference system: a model-free approach, Energy Build., 110 (2016) 182–194.
  22. E.S. Mohamed, G. Papadakis, E. Mathioulakis, V. Belessiotis, A direct-coupled photovoltaic seawater reverse osmosis desalination system toward battery-based systems — a technical and economical experimental comparative study, Desalination, 221 (2008) 17–22.
  23. E.S. Mohamed, G. Papadakis, E. Mathioulakis, V. Belessiotis, An experimental comparative study of the technical and economic performance of a small reverse osmosis desalination system equipped with hydraulic energy recovery unit, Desalination, 194 (2006) 239–250.
  24. E.S. Mohamed, G. Papadakis, E. Mathioulakis, V. Belessiotis, The effect of hydraulic energy recovery in a small sea water reverse osmosis desalination system; experimental and economical evaluation, Desalination, 184 (2005) 241–246.
  25. B. Windrow, M.A. Lehr, 39 Years of Adaptive Neural Networks: Perceptron, Madiline and Backpropagation, Proc. IEEE, Vol. 78, 1990, pp. 1415–1442.
  26. H. Ying, General SISO Takagi–Sugeno fuzzy systems with linear rule consequent are universal approximators, IEEE Trans. Fuzzy Syst., 6 (1998) 582–587.
  27. T. Tagaki, M. Sugeno, Fuzzy Identification of Systems and Its Applications to Modeling and Control, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15, 1985, pp. 116–132.
  28. B. Hartmann, O. Banfer, O. Nelles, A. Sodja, L. Teslic, I. Skrjanc, Supervised hierarchical clustering in fuzzy model identification, IEEE Trans. Fuzzy Syst., 19 (2011) 1163–1176.
  29. MATLAB Manual, Fuzzy Logic Toolbox User’s Guide, The MathWorks Inc., Massachusetts, USA, 2009.
  30. P. Kofinas, G. Vouros, A.I. Dounis, Energy Management in Solar Microgrid via Reinforcement Learning, Proc. 9th Hellenic Conference on Artificial Intelligence (SETN ‘16), Article 12, ACM, Thessaloniki, Greece, 2016, p. 7.