Multivariate statistical monitoring of continuous wastewater treatment plants
(2008) In Engineering Applications of Artificial Intelligence 21(7). p.1080-1091- Abstract
- Abstract in Undetermined
In this paper, different multivariate statistical approaches for analysing wastewater treatment process data are presented and compared. For this purpose, all the methods have been tested using one-year operational data in a simulation model benchmark. The general monitoring strategy adopted includes a screening stage to improve data quality, all adaptive model to detect and diagnose abnormal events, and two complementary tools for helping in the diagnosis of the faults. The first one is based oil the development of a local model that captures the most recent process behaviour and the second one oil the application of fuzzy c-means clustering to the scores of the monitoring model. The results have shown that... (More) - Abstract in Undetermined
In this paper, different multivariate statistical approaches for analysing wastewater treatment process data are presented and compared. For this purpose, all the methods have been tested using one-year operational data in a simulation model benchmark. The general monitoring strategy adopted includes a screening stage to improve data quality, all adaptive model to detect and diagnose abnormal events, and two complementary tools for helping in the diagnosis of the faults. The first one is based oil the development of a local model that captures the most recent process behaviour and the second one oil the application of fuzzy c-means clustering to the scores of the monitoring model. The results have shown that simple scaling parameters adaptation is sufficient to obtain a model useful for monitoring the process during the whole period. Monitoring the deviations from the average daily behaviour showed clear detections of the disturbances in the Hotelling's T(2)-statistic and this feature was useful to determine different operational states (disturbances) in the process by clustering the PCA scores. On the other hand, the proposed procedure for isolation based on a local model improved the diagnosis results in terms of the responsible variables identified and the indication of the beginning of the fault. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/715397
- author
- Aguado, D and Rosén, Christian LU
- organization
- publishing date
- 2008
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Wastewater treatment, Principal component analysis (PCA), Monitoring, Clustering, Benchmark
- in
- Engineering Applications of Artificial Intelligence
- volume
- 21
- issue
- 7
- pages
- 1080 - 1091
- publisher
- Engineering Applications of Artificial Intelligence
- external identifiers
-
- wos:000260748700011
- scopus:52949138733
- ISSN
- 1873-6769
- DOI
- 10.1016/j.engappai.2007.08.004
- language
- English
- LU publication?
- yes
- id
- 63ffefa0-4f9d-4fde-8079-070d0a890140 (old id 715397)
- date added to LUP
- 2016-04-01 11:46:37
- date last changed
- 2022-04-05 04:55:34
@article{63ffefa0-4f9d-4fde-8079-070d0a890140,
abstract = {{Abstract in Undetermined<br/>In this paper, different multivariate statistical approaches for analysing wastewater treatment process data are presented and compared. For this purpose, all the methods have been tested using one-year operational data in a simulation model benchmark. The general monitoring strategy adopted includes a screening stage to improve data quality, all adaptive model to detect and diagnose abnormal events, and two complementary tools for helping in the diagnosis of the faults. The first one is based oil the development of a local model that captures the most recent process behaviour and the second one oil the application of fuzzy c-means clustering to the scores of the monitoring model. The results have shown that simple scaling parameters adaptation is sufficient to obtain a model useful for monitoring the process during the whole period. Monitoring the deviations from the average daily behaviour showed clear detections of the disturbances in the Hotelling's T(2)-statistic and this feature was useful to determine different operational states (disturbances) in the process by clustering the PCA scores. On the other hand, the proposed procedure for isolation based on a local model improved the diagnosis results in terms of the responsible variables identified and the indication of the beginning of the fault.}},
author = {{Aguado, D and Rosén, Christian}},
issn = {{1873-6769}},
keywords = {{Wastewater treatment; Principal component analysis (PCA); Monitoring; Clustering; Benchmark}},
language = {{eng}},
number = {{7}},
pages = {{1080--1091}},
publisher = {{Engineering Applications of Artificial Intelligence}},
series = {{Engineering Applications of Artificial Intelligence}},
title = {{Multivariate statistical monitoring of continuous wastewater treatment plants}},
url = {{http://dx.doi.org/10.1016/j.engappai.2007.08.004}},
doi = {{10.1016/j.engappai.2007.08.004}},
volume = {{21}},
year = {{2008}},
}