Probabilistic parameter estimation of activated sludge processes using Markov Chain Monte Carlo

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

External organisations

  • Civil Engineering, The Catholic University of America
  • DC Water and Sewer Authority
  • Dynamita

Abstract

One of the most important challenges in making activated sludge models (ASMs) applicable to design problems is identifying the values of its many stoichiometric and kinetic parameters. When wastewater characteristics data from full-scale biological treatment systems are used for parameter estimation, several sources of uncertainty, including uncertainty in measured data, external forcing (e.g. influent characteristics), and model structural errors influence the value of the estimated parameters. This paper presents a Bayesian hierarchical modeling framework for the probabilistic estimation of activated sludge process parameters. The method provides the joint probability density functions (JPDFs) of stoichiometric and kinetic parameters by updating prior information regarding the parameters obtained from expert knowledge and literature. The method also provides the posterior correlations between the parameters, as well as a measure of sensitivity of the different constituents with respect to the parameters. This information can be used to design experiments to provide higher information content regarding certain parameters. The method is illustrated using the ASM1 model to describe synthetically generated data from a hypothetical biological treatment system. The results indicate that data from full-scale systems can narrow down the ranges of some parameters substantially whereas the amount of information they provide regarding other parameters is small, due to either large correlations between some of the parameters or a lack of sensitivity with respect to the parameters.

Details

Original languageEnglish
Pages (from-to)254-266
Number of pages13
JournalWater Research
Volume50
Early online date15 Dec 2013
Publication statusPublished - 1 Mar 2014

Keywords

  • ASM, Bayesian, Biological treatment, Markov Chain Monte Carlo, Uncertainty assessment