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.
Original language | English |
---|---|
Pages (from-to) | 2114-2125 |
Journal | Environmental Science: Water Research and Technology |
Volume | 8 |
Issue number | 10 |
DOIs | |
Publication status | Published - 2022 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.