References
- A. Bokhary, A. Tikka, M. Leitch, B.Q. Liao, Membrane fouling
prevention and control strategies in pulp and paper industry
applications: a review, J. Membr. Sci. Res., 4 (2018) 181–187.
- F.L. Wang, V.V. Tarabara, Pore blocking mechanisms during
early stages of membrane fouling by colloids, J. Colloid
Interface Sci., 328 (2008) 464–469.
- E. Iritani, N. Katagiri, Developments of blocking filtration
model in membrane filtration, KONA Powder Part. J., 33 (2016)
179–202.
- N. Delgrange, C. Cabassud, M. Cabassud, L. Durand-Bourlier,
J.M. Lainé, Neural networks for prediction of ultrafiltration
transmembrane pressure – application to drinking water
production, J. Membr. Sci., 150 (1998) 111–123.
- Y.J. Park, Y.J. Choi, S.H. Lee, Analysis of membrane fouling in a
pilot-scale microfiltration plant using mathematical model and
artificial neural network model, Desal. Water Treat., 77 (2017)
69–74.
- Y.-J. Choi, H.J. Oh, S.H. Lee, S.-H. Nam, T.-M. Hwang,
Investigation of the filtration characteristics of pilot-scale
hollow fiber submerged MF system using cake formation
model and artificial neural networks model, Desalination,
297 (2012) 20–29.
- N. Muttil, J.H.W. Lee, Genetic programming for analysis and
real-time prediction of coastal algal blooms, Ecol. Modell.,
189 (2005) 363–376.
- T.-M. Lee, H.J. Oh, Y.-K. Choung, S.H. Oh, M.G. Jeon, J.H. Kim,
S.H. Nam, S.H. Lee, Prediction of membrane fouling in the
pilot-scale microfiltration system using genetic programming,
Desalination, 249 (2009) 285–294.
- 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. Innovation Manage.
Technol., 4 (2013), doi: 10.7763/IJIMT.2013.V4.347.
- M.R. Nikoo, A. Karimi, R. Kerachian, H. Poorsepahy-Samian,
F.H. Daneshmand, Rules for optimal operation of reservoir river-groundwater systems considering water quality targets:
application of M5P model, Eur. Water Resour. Assoc., 27 (2013)
2771–2784.