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Detectability of Fault Signatures in a Wastewater Treatment Process
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-3097-459x
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0001-6101-2863
Örebro University, Sweden. (Center for Applied Autonomous Sensor Systems)ORCID iD: 0000-0002-4651-589X
2022 (English)In: Proceedings of The First SIMS EUROSIM Conference on Modelling and Simulation, SIMS EUROSIM 2021, and 62nd International Conference of Scandinavian Simulation Society, SIMS 2021 / [ed] Esko Juuso, Bernt Lie, Erik Dahlquist and Jari Ruuska, 2022, p. 418-423Conference paper, Published paper (Refereed)
Abstract [en]

In a wastewater treatment plant reliable fault detection is an integral component of process supervision and ensuring safe operation of the process. Detecting and isolating process faults requires that sensors in the process can be used to uniquely identify such faults. However, sensors in the wastewater treatment process operate in hostile environments and often require expensive equipment and maintenance. This work addresses this problem by identifying a minimal set of sensors which can detect and isolate these faults in the Benchmark Simulation Model No. 1.Residual-based fault signatures are used to determine this sensor set using a graph-based approach; these fault signatures can be used in future work developing fault detection methods. It is recommended that further work investigate what sizes of faults are critical to detect based on their potential effects on the process, as well as ways to select an optimal sensor set from multiple valid configurations.

Place, publisher, year, edition, pages
2022. p. 418-423
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 185
Keywords [en]
fault detection, wastewater treatment, detectability, isolation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-58336DOI: 10.3384/ecp21185418ISBN: 978-91-7929-219-5 (print)OAI: oai:DiVA.org:mdh-58336DiVA, id: diva2:1662473
Conference
The First SIMS EUROSIM Conference on Modelling and Simulation, SIMS EUROSIM 2021, and 62nd International Conference of Scandinavian Simulation Society, SIMS 2021, September 21-23, Virtual Conference, Finland
Available from: 2022-05-31 Created: 2022-05-31 Last updated: 2022-12-22Bibliographically approved
In thesis
1. Fault Detection in Wastewater Treatment: Process Supervision to Improve Wastewater Reuse
Open this publication in new window or tab >>Fault Detection in Wastewater Treatment: Process Supervision to Improve Wastewater Reuse
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

As wastewater treatment plants transition to water resource recovery facilities, the need for improved control and consequently supervision increases. Despite the large volume of research that has been performed on this topic, the use in industry is scarce. Practical implementation is challenging due to the nature of the process, and a lack of standardisation in the research results in uncertainty as to the state of the art. This is one of the main challenges identified. 

Experimental work is performed using the Benchmark Simulation Model No. 1 to identify monitoring requirements and evaluate the performance of univariate fault detection methods. For the former, residual based process fault signatures are used to determine minimal sensor requirements based on detectability and isolability goals. Sensor faults are the focus of the latter issue, using the Shewhart, cumulative sum, and exponentially weighted moving average control charts to detect bias and drift faults in a controlled variable sensor. 

The use of a standard model and known fault detection methods is useful to establish a baseline for future work. Given the lack of standardised use in industry this is considered critical. Both proposed methods emphasise ease of visualisation which is beneficial for industrial implementation. 

Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2023
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 332
Keywords
Wastewater treatment, Process supervision, Fault Detection
National Category
Environmental Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-61078 (URN)978-91-7485-575-3 (ISBN)
Presentation
2023-01-27, Delta, Mälardalens universitet, Västerås, 09:30 (English)
Opponent
Supervisors
Available from: 2022-12-01 Created: 2022-11-30 Last updated: 2023-01-09Bibliographically approved

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Marais, Heidi LynnZaccaria, ValentinaNordlander, Eva

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