Uncertainty of a biological nitrogen and phosphorus removal model

Research output: Contribution to conferenceOtherpeer-review

1 Citation (Scopus)


In the last few years, the use of mathematical models in wastewater treatment plant (WWTP) processes has become a common way to predict WWTP behaviour. However, mathematical models generally demand advanced input for their implementation that must be evaluated by an extensive data gathering campaign, which cannot always be carried out. This fact, together with the intrinsic complexity of the model structure, leads to model results that may be very uncertain. Quantification of the uncertainty is imperative. However, despite the importance of uncertainty quantification, only a few studies have been carried out in the wastewater treatment field, and those studies only included a few of the sources of model uncertainty. This paper presents an uncertainty assessment of a mathematical model simulating biological nitrogen and phosphorus removal. The uncertainty assessment was conducted according to the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. The model was based on Activated-Sludge Models 1 (ASM) and 2 (ASM2). Different approaches can be used for uncertainty analysis. In the present study, the GLUE procedure was employed. The GLUE methodology requires a large number of Monte Carlo simulations in which a random sampling of individual parameters drawn from probability distributions is used to determine a set of parameter values. Using this approach, model reliability was evaluated based on its capacity to globally limit the uncertainty. The method was applied to a full-scale WWTP for which quantity-quality data were gathered.
Original languageEnglish
Number of pages9
Publication statusPublished - 2010

All Science Journal Classification (ASJC) codes

  • Software
  • Environmental Engineering
  • Modelling and Simulation

Fingerprint Dive into the research topics of 'Uncertainty of a biological nitrogen and phosphorus removal model'. Together they form a unique fingerprint.

Cite this