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Functional Data Analysis for Detecting Faults in Water and Wastewater Treatment.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Functional Data Analysis for Detecting Faults in Water and Wastewater Treatment./
Author:	Kuras, Aurora.
Description:	1 online resource (53 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-04.
Contained By:	Masters Abstracts International84-04.
Subject:	Environmental engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29067625click for full text (PQDT)
ISBN:	9798351481937

Functional Data Analysis for Detecting Faults in Water and Wastewater Treatment.
Kuras, Aurora.

Functional Data Analysis for Detecting Faults in Water and Wastewater Treatment. - 1 online resource (53 pages)

Source: Masters Abstracts International, Volume: 84-04.

Thesis (M.S.)--Colorado School of Mines, 2022.

Includes bibliographical references

Early and effective fault detection in water and wastewater treatment plants is important to maintain water quality and prevent process disruptions. Some faults, such as spike faults, are easily detected with traditional fault detection methods that identify extreme values, while other faults, such as drift faults, are difficult to identify due to their slowly changing behavior. In addition, there is the need for methods that assist operator decision making and have straightforward interpretability. This study applies a method in functional data analysis (FDA) for fault detection to drift faults observed in a sequencing batch membrane bioreactor and closed circuit reverse osmosis system. FDA enables analysis of cyclic data, which are curves or functions produced by system with repetitive behavior over a time period or process. Fault detection in a set of curves can be accomplished through the computation of statistics describing their shapes and magnitudes. In addition, functional plots visually supplement alarm results to assist operators. In this study we apply an existing FDA method for retrospective outlier detection and extend it for the non-stationary, real-time applications required for tracking water and wastewater process data. We demonstrate its ability to identify drifts faults in early stages as well as spike faults for three case studies analyzed.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798351481937Subjects--Topical Terms:

548583
Environmental engineering.
Subjects--Index Terms:

Functional data analysisIndex Terms--Genre/Form:

542853
Electronic books.

Functional Data Analysis for Detecting Faults in Water and Wastewater Treatment.
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245 1 0 $a Functional Data Analysis for Detecting Faults in Water and Wastewater Treatment.
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500 $a Source: Masters Abstracts International, Volume: 84-04.
500 $a Advisor: Cath, Tzahi Y. ; Hering, Amanda S.
502 $a Thesis (M.S.)--Colorado School of Mines, 2022.
504 $a Includes bibliographical references
520 $a Early and effective fault detection in water and wastewater treatment plants is important to maintain water quality and prevent process disruptions. Some faults, such as spike faults, are easily detected with traditional fault detection methods that identify extreme values, while other faults, such as drift faults, are difficult to identify due to their slowly changing behavior. In addition, there is the need for methods that assist operator decision making and have straightforward interpretability. This study applies a method in functional data analysis (FDA) for fault detection to drift faults observed in a sequencing batch membrane bioreactor and closed circuit reverse osmosis system. FDA enables analysis of cyclic data, which are curves or functions produced by system with repetitive behavior over a time period or process. Fault detection in a set of curves can be accomplished through the computation of statistics describing their shapes and magnitudes. In addition, functional plots visually supplement alarm results to assist operators. In this study we apply an existing FDA method for retrospective outlier detection and extend it for the non-stationary, real-time applications required for tracking water and wastewater process data. We demonstrate its ability to identify drifts faults in early stages as well as spike faults for three case studies analyzed.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Environmental engineering. $3 548583
650 4 $a Statistics. $3 517247
650 4 $a Water resources management. $3 794747
653 $a Functional data analysis
653 $a Wastewater treatment
653 $a Water treatment
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710 2 $a Colorado School of Mines. $b Civil and Environmental Engineering. $3 3183130
773 0 $t Masters Abstracts International $g 84-04.
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