Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Identification of behavioural model input data sets for WWTP uncertainty analysis

Lindblom, E. LU ; Jeppsson, U. LU and Sin, G. (2020) In Water science and technology : a journal of the International Association on Water Pollution Research 81(8). p.1558-1568
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... (More)

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.

(Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Water science and technology : a journal of the International Association on Water Pollution Research
volume
81
issue
8
pages
11 pages
publisher
IWA Publishing
external identifiers
  • scopus:85087821429
  • pmid:32644949
ISSN
0273-1223
DOI
10.2166/wst.2019.427
language
English
LU publication?
yes
id
2fb14da4-7a0c-4b11-b05b-ab482568da8c
date added to LUP
2020-07-22 12:38:13
date last changed
2024-02-16 19:42:57
@article{2fb14da4-7a0c-4b11-b05b-ab482568da8c,
  abstract     = {{<p>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.</p>}},
  author       = {{Lindblom, E. and Jeppsson, U. and Sin, G.}},
  issn         = {{0273-1223}},
  language     = {{eng}},
  number       = {{8}},
  pages        = {{1558--1568}},
  publisher    = {{IWA Publishing}},
  series       = {{Water science and technology : a journal of the International Association on Water Pollution Research}},
  title        = {{Identification of behavioural model input data sets for WWTP uncertainty analysis}},
  url          = {{http://dx.doi.org/10.2166/wst.2019.427}},
  doi          = {{10.2166/wst.2019.427}},
  volume       = {{81}},
  year         = {{2020}},
}