Statistical analysis of fine resolution flow datasets helps characterizing flow behaviour in primary clarifiers: a decision support method

In situ measurement campaigns of primary clarifiers are rarely implemented properly because of their cost, time, and energy demand. Hydrodynamic modelling possibilities for such reactors have been intensely examined recently, but on-site factors affecting flow characteristics (e.g. flow distributors...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Katalin Kiss, Miklós Patziger
Formato: article
Lenguaje:EN
Publicado: IWA Publishing 2021
Materias:
Acceso en línea:https://doaj.org/article/2af012d4a32b425483f5a4ef23cb18c9
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:2af012d4a32b425483f5a4ef23cb18c9
record_format dspace
spelling oai:doaj.org-article:2af012d4a32b425483f5a4ef23cb18c92021-11-05T21:08:55ZStatistical analysis of fine resolution flow datasets helps characterizing flow behaviour in primary clarifiers: a decision support method1751-231X10.2166/wpt.2021.006https://doaj.org/article/2af012d4a32b425483f5a4ef23cb18c92021-04-01T00:00:00Zhttp://wpt.iwaponline.com/content/16/2/420https://doaj.org/toc/1751-231XIn situ measurement campaigns of primary clarifiers are rarely implemented properly because of their cost, time, and energy demand. Hydrodynamic modelling possibilities for such reactors have been intensely examined recently, but on-site factors affecting flow characteristics (e.g. flow distributors) have not received sufficient attention. This paper describes the use of ANOVA in examining fine resolution flow datasets and the related decision support method for in situ measurement campaigns and subsequent modelling processes. The characteristics of the flow and the applicability of 2D and 3D methods to investigate hydrodynamic features are discussed through the example of a rectangular primary clarifier, also considering the reproducibility of measurements ranging from typical nominal flow rates to peak loads. Based on the data, recommendations are provided on the adequate sizing of a measurement campaign, potentially reduced to a single longitudinal section (2D measurement). According to our results, performing hydrodynamic measurements with a 2D-arrangement of measuring points is sufficient in the case of such clarifiers, also with regard to the design processes. When applying the described methods, the related efforts and costs may be reduced and estimated more easily. However, care should be taken when applying this method to determine the spacing of measuring points correctly. Highlights Uncertainties of in situ measurement and CFD modelling of a primary clarifier.; Decision support method based on a statistical analysis (ANOVA) is introduced.; A rectangular PST is examined ranging from typical nominal flow rates to peak loads.; 2D-arrangement of measuring points (single longitudinal section) is sufficient.; Near the bottom, the distance between measuring points should not exceed 0.3 m.;Katalin KissMiklós PatzigerIWA Publishingarticleanovaflow characteristicsflow measurementprimary clarifierEnvironmental technology. Sanitary engineeringTD1-1066ENWater Practice and Technology, Vol 16, Iss 2, Pp 420-435 (2021)
institution DOAJ
collection DOAJ
language EN
topic anova
flow characteristics
flow measurement
primary clarifier
Environmental technology. Sanitary engineering
TD1-1066
spellingShingle anova
flow characteristics
flow measurement
primary clarifier
Environmental technology. Sanitary engineering
TD1-1066
Katalin Kiss
Miklós Patziger
Statistical analysis of fine resolution flow datasets helps characterizing flow behaviour in primary clarifiers: a decision support method
description In situ measurement campaigns of primary clarifiers are rarely implemented properly because of their cost, time, and energy demand. Hydrodynamic modelling possibilities for such reactors have been intensely examined recently, but on-site factors affecting flow characteristics (e.g. flow distributors) have not received sufficient attention. This paper describes the use of ANOVA in examining fine resolution flow datasets and the related decision support method for in situ measurement campaigns and subsequent modelling processes. The characteristics of the flow and the applicability of 2D and 3D methods to investigate hydrodynamic features are discussed through the example of a rectangular primary clarifier, also considering the reproducibility of measurements ranging from typical nominal flow rates to peak loads. Based on the data, recommendations are provided on the adequate sizing of a measurement campaign, potentially reduced to a single longitudinal section (2D measurement). According to our results, performing hydrodynamic measurements with a 2D-arrangement of measuring points is sufficient in the case of such clarifiers, also with regard to the design processes. When applying the described methods, the related efforts and costs may be reduced and estimated more easily. However, care should be taken when applying this method to determine the spacing of measuring points correctly. Highlights Uncertainties of in situ measurement and CFD modelling of a primary clarifier.; Decision support method based on a statistical analysis (ANOVA) is introduced.; A rectangular PST is examined ranging from typical nominal flow rates to peak loads.; 2D-arrangement of measuring points (single longitudinal section) is sufficient.; Near the bottom, the distance between measuring points should not exceed 0.3 m.;
format article
author Katalin Kiss
Miklós Patziger
author_facet Katalin Kiss
Miklós Patziger
author_sort Katalin Kiss
title Statistical analysis of fine resolution flow datasets helps characterizing flow behaviour in primary clarifiers: a decision support method
title_short Statistical analysis of fine resolution flow datasets helps characterizing flow behaviour in primary clarifiers: a decision support method
title_full Statistical analysis of fine resolution flow datasets helps characterizing flow behaviour in primary clarifiers: a decision support method
title_fullStr Statistical analysis of fine resolution flow datasets helps characterizing flow behaviour in primary clarifiers: a decision support method
title_full_unstemmed Statistical analysis of fine resolution flow datasets helps characterizing flow behaviour in primary clarifiers: a decision support method
title_sort statistical analysis of fine resolution flow datasets helps characterizing flow behaviour in primary clarifiers: a decision support method
publisher IWA Publishing
publishDate 2021
url https://doaj.org/article/2af012d4a32b425483f5a4ef23cb18c9
work_keys_str_mv AT katalinkiss statisticalanalysisoffineresolutionflowdatasetshelpscharacterizingflowbehaviourinprimaryclarifiersadecisionsupportmethod
AT miklospatziger statisticalanalysisoffineresolutionflowdatasetshelpscharacterizingflowbehaviourinprimaryclarifiersadecisionsupportmethod
_version_ 1718444027061207040