Identification of behavioural model input data sets for WWTP uncertainty analysis

E. Lindblom*, U. Jeppsson, G. Sin

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Uncertainty analysis is important for wastewater treatment plant (WWTP) model applications. An important aspect of uncertainty analysis is the identification and proper quantification of sources of uncertainty. In this contribution, a methodology to identify an ensemble of behavioural model representations (combinations of input data, model structure and parameter values) is presented and evaluated. The outcome is a multivariate conditional distribution of input data that is used for generating samples of likely inputs (such as Monte Carlo input samples) to perform WWTP model uncertainty analysis. This article presents an approach to verify uncertainty distributions of input data (otherwise often assumed) by using historical observations and actual plant data.
Original languageEnglish
JournalWater Science and Technology
Volume81
Issue number8
Pages (from-to)1558–1568
ISSN0273-1223
DOIs
Publication statusPublished - 2020

Bibliographical note

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).

Keywords

  • BSM
  • Calibration
  • Influent data
  • Monte carlo simulation
  • Modelling

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