DOI: 10.1016/j.watres.2017.11.023
Scopus记录号: 2-s2.0-85034630433
论文题名: Predicting of ultrafiltration performances by advanced data analysis
作者: Teychene B. ; Touffet A. ; Baron J. ; Welte B. ; Joyeux M. ; Gallard H.
刊名: Water Research
ISSN: 431354
出版年: 2018
卷: 129 起始页码: 365
结束页码: 374
语种: 英语
英文关键词: ARIMA model
; Cluster analysis
; Fluorescence excitation emission matrix
; Karst area
; Principal component analysis
; Ultrafiltration
Scopus关键词: Boilers
; Chemical analysis
; Cluster analysis
; Decision making
; Fluorescence
; Forecasting
; Groundwater
; Groundwater resources
; Information analysis
; Information management
; Matrix algebra
; Membrane fouling
; Potable water
; Principal component analysis
; Proteins
; Ultrafiltration
; Water filtration
; Water quality
; ARIMA modeling
; Drinking water production
; Filtration performance
; Fluorescence excitation emission matrix
; Karst areas
; Multiple linear regressions
; Physical and chemical properties
; Water quality degradation
; Quality control
; carbon
; drinking water
; ground water
; protein
; cluster analysis
; drinking water
; filtration
; fluorescence
; groundwater resource
; hydrological modeling
; karst hydrology
; matrix
; optimization
; parameter estimation
; performance assessment
; physicochemical property
; prediction
; principal component analysis
; protein
; resource management
; ultrafiltration
; water management
; water quality
; Article
; biofouling
; chemical analysis
; cluster analysis
; data analysis
; France
; physical chemistry
; priority journal
; protein content
; ultrafiltration
; water quality
; water supply
; artificial membrane
; chemistry
; decision making
; devices
; principal component analysis
; regression analysis
; spectrofluorometry
; statistics and numerical data
; ultrafiltration
; water management
; France
; Ile de France
; Paris
; Ville de Paris
; Carbon
; Cluster Analysis
; Decision Making
; Drinking Water
; Membranes, Artificial
; Paris
; Principal Component Analysis
; Proteins
; Regression Analysis
; Spectrometry, Fluorescence
; Ultrafiltration
; Water Purification
; Water Quality
英文摘要: In order to optimize drinking water production operation, membrane users can use several analytical tools that help membrane fouling prediction and alleviate fouling by a proper feed water resource selection. However, during strong fouling event, membrane decision-makers still face short-term deadline to decide between different options (e.g. optimization of pretreatment or change in feed water quality). Hence, statistical approach might help to better select the most relevant analytical parameter related to fouling potential of a specific resource in order to speed-up decision taking. In this study, the physical and chemical properties and the filtration performances (at lab-scale) of five ground water resources, selected as potential resources of a large drinking production site of Paris (France), was evaluated through one year. Principal component analysis emphasizes the strong link between waters’ organic matrix and fouling propensity. Cluster analysis of filtration performances allowed classifying the water samples into three groups exhibiting strong, low and intermediate fouling. Finally, multiple linear regressions performed on all collected data indicated that strong fouling events were related to a combined increase of carbon content and protein like-substances while intermediate fouling might only be anticipated by an increase of fluorescence signal associated to protein like-substances. This study demonstrates that advanced data analysis might be a powerful tool to better manage water resources selection used for drinking water production and to forecast filtration performances in a context of water quality degradation. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/113093
Appears in Collections: 气候减缓与适应
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作者单位: Institut de Chimie des Milieux et des Matériaux de Poitiers (UMR CNRS 7285), École Nationale Supérieure d'Ingénieurs de Poitiers (ENSIP), Université de Poitiers, 1 rue Marcel Doré, Bâtiment 1, Poitiers Cedex, 86022, France; Direction de la Recherche et du Développement de la Qualité de l'Eau, Eau de Paris, 33 avenue Jean Jaurès, Ivry sur Seine, 94200, France
Recommended Citation:
Teychene B.,Touffet A.,Baron J.,et al. Predicting of ultrafiltration performances by advanced data analysis[J]. Water Research,2018-01-01,129