A neural network system for modelling of coagulant dosage used in drinking water treatment

  • B. Lamrini
  • A. Benhammou
  • A. Karama
  • M-V. Le Lann
Conference paper


This paper presents the elaboration and validation of “soft sensor” using neural networks for on-line estimation of the coagulation dose from raw water characteristics. The main parameters influencing the coagulant dosage are firstly determined via a PCA. A brief description of the methodology used for the synthesis of neural model is given and experimental results are included. The training of the neural network is performed using the Weight Decay regularization in combination with Levenberg-Marquardt method. The performance of this soft sensor is illustrated with real data.


Total Suspend Solid Water Treatment Plant Neural Model Drinking Water Treatment Neural Network System 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Canu, S., Ding, X., Granvalet, Y (1997) Statistiques et Méthodes Neuronales: Une application des réseaux de neurones pour la prévision à un pas de temps. Thiria, S., Lechevallier, Y., Gascul, O., Canu, S. (eds) Chapitre 7, Dunod, Paris, pp. 120–131.Google Scholar
  2. [2]
    Canu, S., Sobral, R., Lengellé, R (1990) Formel neural network as an adaptative model for water demand. Proceedings of INNC’90. Kluwer Academic Publishers 1: 131–136.Google Scholar
  3. [3]
    Gagnon, C., Grandjean, B.P.A, Thibault, J (1997) Modelling of coagulant dosage in a water treatment plant. Artificial Intelligence in Engineering 11: 401–404.CrossRefGoogle Scholar
  4. [4]
    Adgar, A., Cox, C.S., Daniel, P.R., Lowdon, A (1996) Chemical dosing philosophies for a water treatment plant: results of some pilot plant experimentation. In Proceeding of the IEE Conference Publication 427(2): 1052–1057.Google Scholar
  5. [5]
    Evans, J., Enoch, C., Johnson, M., Williams, P (1998) Intelligent based auto-coagulation control applied to a water treatment works. In Proceedings of International Conference on Control: 141–145.Google Scholar
  6. [6]
    Rumelhart, D.E., McClelland, J.L (1986) Parallel distribution processing: exploration in the microstructure of cognition. Cambridge. MA: MIT Press 1.Google Scholar
  7. [7]
    Mackay, D.J.C (1992a) Bayesian interpolation. Neural Computation 4: 415–447.Google Scholar
  8. [8]
    Mackay, D.J.C (1992b) A pratical Bayesian Framework for backpropagation Networks. Neural Computation 4: 448–472.Google Scholar
  9. [9]
    Hinton, G.E (1987) Learning Translation Invariant Recognition in Massively Parallel Networks. Proceedings PARLE Conference on Parallel Architectures and Languages Europe. Bakker, J.W., Nijman, A.J., Treleaven, P.C. (Eds.), Springer-Verlag, Berlin, pp. 1–13.Google Scholar
  10. [10]
    Gallinari, P., Cibas, T (1999) Practical complexity control in multilayer perceptrons. Signal Processing 74: 29–46.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • B. Lamrini
    • 1
  • A. Benhammou
    • 1
  • A. Karama
    • 1
  • M-V. Le Lann
    • 2
    • 3
  1. 1.Laboratoire d’Automatique et d’Etude des ProcédésFaculty of Sciences SemlaliaMorocco
  2. 2.LAAS/CNRSToulouse cedex 4France
  3. 3.INSA, DGEIToulouse cedex 4France

Personalised recommendations