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Gaussian process regression for monitoring and fault detection of wastewater treatment processes
Uppsala University, Sweden. (Division of Systems and Control, Department of Information Technology)
IVL Swedish Environmental Research Institute, Sweden.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-8034-4043
Uppsala University, Sweden.
(English)In: Water Science and Technology, ISSN 0273-1223, E-ISSN 1996-9732Article in journal (Refereed) Epub ahead of print
Abstract [en]

Monitoring and fault detection methods are increasingly important to achieve a robust and resource efficient operation of wastewater treatment plants (WWTPs). The purpose of this paper was to evaluate a promising machine learning method, Gaussian process regression (GPR), at WWTP monitoring applications. We evaluated GPR at two WWTP monitoring problems: estimate missing data in a flow rate signal (simulated data), and detect a drift in an ammonium sensor (real data). We showed that GPR with the standard estimation method, maximum likelihood estimation (GPR-MLE), suffered from local optima during estimation of kernel parameters, and did not give satisfactory results in a simulated case study. However, GPR with a state-of-the-art estimation method based on sequential Monte Carlo estimation (GPR-SMC) gave good predictions and did not suffer from local optima. Comparisons with simple standard methods revealed that GPR-SMC performed better than linear interpolation in estimating missing data in a noisy flow rate signal. We conclude that GPR-SMC is both a general and powerful method for monitoring full-scale WWTPs. However, this paper also shows that it does not always pay off to use more sophisticated methods. New methods should be critically compared against simpler methods, which might be good enough for some scenarios.

Place, publisher, year, edition, pages
UK: IWA Publishing.
Keyword [en]
Bayesian regression, Gaussian processes, kernel, machine learning, missing data, process monitoring
National Category
Environmental Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-35271DOI: 10.2166/wst.2017.162OAI: oai:DiVA.org:mdh-35271DiVA: diva2:1092177
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Funder
Knowledge Foundation, 20140168
Available from: 2017-05-01 Created: 2017-05-01 Last updated: 2017-05-03Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf