Abstract
Ammonium (NH4 +) load based aeration control on biological wastewater treatment plants saves costs and enhances nitrogen removal. However, the need for maintenance intensive NH4+ sensors hamper the controls application in practice. Alternatives, in the form of soft-sensors are broadly discussed in academia. A soft-sensor recently described in literature exploits the pH effects induced by biological NH4 + oxidation. This concept is now further developed by means of qualitative trend analysis (QTA). Previously, the qualitative path estimation (QPE) algorithms was proposed as a fast and reliable QTA algorithm for batch process data analysis. It does not allow online application in continuous flow systems however. In this work, a modification of QPE, call qualitative state estimation (QSE), is proposed as a suitable algorithm for continuous-flow systems. Initial tests indicate that the QSE algorithms is a robust technique for extraction of relevant information in a full-scale environment. At the WWTP Hard in Winterthur, this resulted in cost-saving automation of the aeration system. This contribution summarizes these first results.
Original language | English |
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Title of host publication | Computer Aided Chemical Engineering |
Publisher | Elsevier B.V. |
Pages | 2531-2536 |
Number of pages | 6 |
DOIs | |
State | Published - 2015 |
Externally published | Yes |
Publication series
Name | Computer Aided Chemical Engineering |
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Volume | 37 |
ISSN (Print) | 1570-7946 |
Funding
This work was supported by the Commission for Technology and Innovation (CTI) of the Swiss Federal Department of Economic Affairs Education and Research (EAER). (CTI project no. 14351. PFIW-IW). The authors thank the staff of the WWTP Hard in Winterthur for the opportunity and assistance with this study.
Keywords
- Ammonia control
- Biological wastewater treatment
- Hidden Markov model
- Kernel regression
- Process monitoring