Ammonium Estimation in a Biological Wastewater Plant Using Feedforward Neural Networks

  • Hilario López García
  • Iván Machón González
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4087)


Mathematical models are normally used to calculate the component concentrations in biological wastewater treatment. However, this work deals with the wastewater from a coke plant and it implies inhibition effects between components which do not permit the use of said mathematical models. Due to this, feed-forward neural networks were used to estimate the ammonium concentration in the effluent stream of the biological plant. The architecture of the neural network is based on previous works in this topic. The methodology consists in performing a group of different sizes of the hidden layer and different subsets of input variables.


Chemical Oxygen Demand Hide Layer Ammonium Concentration Hide Neuron Coke Plant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366 (1989)CrossRefGoogle Scholar
  2. 2.
    Funahashi, K.: On the approximate realization of continuous mappings by neural networks. Neural Networks 2, 192–193 (1989)CrossRefGoogle Scholar
  3. 3.
    Cybenko, G.: Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems 2, 303–314 (1989)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Hartman, E., Keeler, J., Kowalski, J.: Layered neural networks with gaussian hidden units as universal approximations. Neural Computation 2, 210–215 (1990)CrossRefGoogle Scholar
  5. 5.
    Levenberg, K.: A method for the solution of certain problems in least squares. Quart. Appl. Math., 164–168 (1944)Google Scholar
  6. 6.
    Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11, 431–441 (1963)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Moré, J.: The levenberg-marquardt algorithm: Implementation and theory. Numerical Analysis 630, 105–116 (1977)CrossRefGoogle Scholar
  8. 8.
    LeCun, Y., Denker, J., Solla, S.: Optimal brain damage. Advances in Neural Information Processing Systems 2, 598–605 (1990)Google Scholar
  9. 9.
    López, H., González, R., Machón, I., Ojea, G., Peregrina, S., González, J., de Abajo, N.: Identification of melting and brightening section of a tinplate facility by means of neural networks. In: Proc. European Control Conference, Porto (2001)Google Scholar
  10. 10.
    Norgaard, M., Ravn, O., Poulsen, N., Hansen, L.: Neural Networks for Modelling and Control of Dynamic Systems. Springer, London (2000)Google Scholar
  11. 11.
    Norgaard, M., Ravn, O., Poulsen, N.: Nnsysid-toolbox for system identification with neural networks. Mathematical and Computer Modelling of Dynamical Systems 8, 1–20 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hilario López García
    • 1
  • Iván Machón González
    • 1
  1. 1.Escuela Politécnica Superior de Ingeniería. Departamento de Ingeniería Eléctrica, Electrónica de Computadores y Sistemas. Edificio DepartamentalUniversidad de OviedoGijónSpain

Personalised recommendations