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Development of a Neural Sensor for On-Line Prediction of Coagulant Dosage in a Potable Water Treatment Plant in the Way of Its Diagnosis

  • Hector Hernandez
  • Marie-Véronique Le Lann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4140)

Abstract

Coagulation is one of the most important stages in surface water treatment, allowing for the removal of colloidal particles. Its control, in the majority of the plants remains still manual and requires long and expensive analyses of laboratory which provide only periodic information. The present work describes an innovative methodology integrating various techniques for the development of a diagnosis system of this plant. A first part, concerned by this paper, consisted in developing a software sensor based on artificial neural networks for predicting on-line the amount of optimal coagulant dosage from raw water characteristics such as turbidity, pH, temperature, etc. In a second part this information will be integrated like an input to a diagnosis system of the plant. The development of the neural sensor has been performed based in real data covering several years of operation and its performance have been compared toward those given by a multi-linear interpolation.

Keywords

Hide Layer Total Solid Suspend Water Treatment Plant Coagulant Dosage Online Prediction 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hector Hernandez
    • 1
  • Marie-Véronique Le Lann
    • 1
    • 2
  1. 1.Laboratoire d’Analyse et d’Architecture des Systèmes, LAAS-CNRSToulouseFrance
  2. 2.Département de Génie Electrique et InformatiqueUniversité de Toulouse, INSAToulouseFrance

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