Application of data reconciliation to a dynamically operated wastewater treatment process with off-gas measurements

Journal Article (2022)
Authors

Quan H. Le (Universiteit Gent)

Ir P.J.T. Verheijen (TU Delft - BT/Design and Engineering Education)

M. C M van Loosdrecht (TU Delft - BT/Environmental Biotechnology)

E. I.P. Volcke (Universiteit Gent)

Research Group
BT/Design and Engineering Education
Copyright
© 2022 Quan H. Le, Peter J.T. Verheijen, Mark C.M. van Loosdrecht, Eveline I.P. Volcke
To reference this document use:
https://doi.org/10.1039/d2ew00006g
More Info
expand_more
Publication Year
2022
Language
English
Copyright
© 2022 Quan H. Le, Peter J.T. Verheijen, Mark C.M. van Loosdrecht, Eveline I.P. Volcke
Research Group
BT/Design and Engineering Education
Issue number
10
Volume number
8
Pages (from-to)
2114-2125
DOI:
https://doi.org/10.1039/d2ew00006g
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

This study deals with the application of data reconciliation to wastewater treatment processes which are subject to dynamic conditions and therefore do not reach a steady-state behaviour sensu stricto. The SHARON partial nitritation process, which is operated cyclically with alternating aerated and anoxic periods, is studied as an example. The collected data long-term dynamic data set was split up into data subsets corresponding with different pseudo-steady-state operations, which allowed a better gross error detection. Mass balances were set up taking into account off-gas measurements besides liquid phase measurements and including kinetic relations between measurements based on the biological conversions in the reactor. As a result, a higher number of variables could be reconciled, more key variables could be identified, and gross error detection was facilitated. In order to draw conclusions on the process performance in a shorter period of operation, e.g., on the N2O emission factor, the average value of the whole data set should be used with caution. The strong dependence of infiltrated air on the aeration regime and gross error in grab sampling (magnitude of 20%) had a substantial impact on calculating N2O emission. It is recommended that the process performance indicators are derived and checked separately for steady state data subsets to guarantee reliable outcomes.

Files

D2ew00006g.pdf
(pdf | 1.23 Mb)
- Embargo expired in 01-07-2023
License info not available