References

  1. J.M. Laine, D. Vial, P. Moulart, Status after 10 years of operationoverview of UF technology today, Desalination, 131 (2000) 17–25.
  2. B. Van der Bruggen, C. Vandecasteele, T.V. Gestel, W. Doyen, R. Leysen, A review of pressure-driven membrane processes in wastewater treatment and drinking water production, Environ. Prog., 22 (2003) 46–56.
  3. S. Chellam, J.G. Jacangelo, T.P. Bonacquisti, Modeling and experimental verification of pilot-scale hollow fiber, direct flow microfiltration with periodic backwashing, Environ. Sci. Technol., 32 (1998) 75–81.
  4. K.J. Howe, M.M. Clark, Fouling of Microfiltration and Ultrafiltration Membranes by Natural Waters, Environ. Sci. Technol., 36 (2002) 3571–3576.
  5. R.H. Peiris, C. Halle, H. Budman, C. Moresoli, S. Peldszus, P.M. Huck, R.L. Legge, Identifying fouling events in a membranebased drinking water treatment process using principal component analysis of fluorescence excitation–emission matrices, Water Res., 44 (2010) 185–194.
  6. L. Wang, X. Wang, K. Fukushi, Effects of operational conditions on ultrafiltration membrane fouling, Desalination, 229 (2008) 181–191.
  7. N. Lee, G. Amy, J. Croue, Low-pressure membrane (MF/UF) fouling associated with allochthonous versus autochthonous natural organic matter, Water Res., 40 (2006) 2357–2368.
  8. W. Gao, H. Liang, J. Ma, M. Han, Z.-l. Chen, Z.-s. Han, G.-b. Li, Membrane fouling control in ultrafiltration technology for drinking water production: a review, Desalination, 272 (2011) 1–8.
  9. S. Babel, S. Takizawa, H. Ozaki, Factors affecting seasonal variation of membrane filtration resistance caused by Chlorella algae, Water Res., 36 (2002) 1193–1202.
  10. Q.F. Liu, S.H. Kim, S. Lee, Prediction of microfiltration membrane fouling using artificial neural network models, Sep. Purif. Technol., 70 (2009) 96–102.
  11. F. Schmitt, R. Banuc, I.T. Yeom, K.U. Do, Development of artificial neural networks to predict membrane fouling in an anoxic-aerobic membrane bioreactor treating domestic wastewater, Biochem. Eng. J., 133 (2018) 47–58.
  12. H.I. Witten, E. Frank, M.A. Hall, Data Mining, Principle Machine Learning Tools and Techniques, Burlington, MA, 2011.
  13. J.R. Quinlan, Learning with Continuous Classes, in: Proc. 5th Australian Joint Conference on Artificial Intelligence, World Scientific, Singapore, 1992, pp. 343–348.
  14. Y. Wang, I.H. Witten, Inducing Model Trees for Continuous Classes, in Proc. 9th European Conference on Machine Learning, 1997.
  15. B. Bhattacharya, D.P. Solomatine, Neural networks and M5P model trees in modelling water level-discharge relationship, Neurocomputing, 63 (2005) 381–396.
  16. D.P. Solomatine, Optimisation of hierarchical modular models and M5 trees, in Proc. International Joint Conference on Neural Networks, Budapest, Hungary, 2004.
  17. E.K. Onyari, F.M. Ilunga, Application of MLP neural network and M5P model tree in predicting streamflow: a case study of Luvuvhu Catchment, South Africa, Int. J. Innov. Manage. Technol., 4 (2013) 1–15.
  18. M. Dalmau, N. Atanasova, S. Gabarrón, I.R. Roda, J. Comas, Comparison of a deterministic and a data driven model to describe MBR fouling, Chem. Eng. J., 260 (2015) 300–308.