Good modelling practice in drinking water treatment, applied to Weesperkarspel plant of Waternet

  • Authors:

  • L. C. Rietveld;A. W. C. van der Helm;K. M. van Schagen;L. T. J. van der Aa

  • Affiliations:

  • Delft University of Technology, Department of Water Management, PO Box 5048, 2600 GA Delft, The Netherlands and Waternet, PO Box 8169, 1005 AD Amsterdam, The Netherlands;Delft University of Technology, Department of Water Management, PO Box 5048, 2600 GA Delft, The Netherlands and Waternet, PO Box 8169, 1005 AD Amsterdam, The Netherlands;DHV BV, PO Box 1132, 3800 BC Amersfoort, The Netherlands and Delft University of Technology, Delft Center for Systems and Control Mekelweg 2, 2628 CD Delft, The Netherlands;Waternet, PO Box 8169, 1005 AD Amsterdam, The Netherlands

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2010

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Abstract

Good modelling practice increases the credibility and impact of the information and insight that modelling aims to generate. It is known to be crucial for model acceptance and it is a necessity to amass a long-term, systematic thorough knowledge base for both science and decision making. This paper shows how ten steps in model development and evaluation can also be applied to numerical modelling of drinking water treatment, using models of drinking water treatment processes of the Weesperkarspel treatment plant of Waternet. The Weesperkarspel plant consists of ozonation, pellet softening, biological activated carbon filtration and slow sand filtration. For the different processes models were developed that were used for operational improvements. The modelling resulted in new insights and knowledge about the treatment processes and improved operation of the processes. From scenario studies for the pellet softening it was concluded that chemical dosing can be diminished when by-pass ratio is increased and that pellet size can be controlled by measuring the difference in pressure guaranteeing fluidisation of the pellet bed. In addition, ozone dosage can be optimised by modelling ozone exposure, bromate formation and biologically degradable natural organic matter (NOM) under varying influent water quality.