Global sensitivity analysis in ASM applications: comparison ofthe SRC and Extended-FAST method for a UCT-MBR model

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Abstract

In this study global sensitivity analysis is performed to identify influential as well as non-influentialparameters in a model of a University Cape Town Membrane Bioreactor (UCT-MBR). Inparticular, the Standardised Regression Coefficients (SRC) and Extended-FAST sensitivity analysismethods are applied. The sensitivity of model variables towards parameter variation is analysed forCODTOT, SNH4, SNO3, SPO, and MLSS along five reactor compartments. Both methods indicate that theparameters identified as being influential differ from section to section due to the differentprocesses involved. Moreover, the relevant influence of the membrane filtration parameters isdetected in the first plant section due to the influence of the recycled sludge. It is found that thecomputationally less expensive SRC method is applied outside its range of applicability with R2 =(0.3-0.6) < 0.7. Nevertheless, the ranking obtained with the SRC method for the influentialparameters is very similar to that of the Extended-FAST method, except for MLSS. However, toobtain reliable quantitative information on variance decomposition and to detect and quantify (insome cases considerable) interactions present among parameters the use of the computationallymore expensive Extended-FAST is found to be necessary in this case study.
Lingua originaleEnglish
Pagine543-552
Numero di pagine10
Stato di pubblicazionePublished - 2011

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bioreactor
sensitivity analysis
membrane
ranking
sludge
decomposition
comparison
method
parameter

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@conference{8205744658c34192b5098b701a79e090,
title = "Global sensitivity analysis in ASM applications: comparison ofthe SRC and Extended-FAST method for a UCT-MBR model",
abstract = "In this study global sensitivity analysis is performed to identify influential as well as non-influentialparameters in a model of a University Cape Town Membrane Bioreactor (UCT-MBR). Inparticular, the Standardised Regression Coefficients (SRC) and Extended-FAST sensitivity analysismethods are applied. The sensitivity of model variables towards parameter variation is analysed forCODTOT, SNH4, SNO3, SPO, and MLSS along five reactor compartments. Both methods indicate that theparameters identified as being influential differ from section to section due to the differentprocesses involved. Moreover, the relevant influence of the membrane filtration parameters isdetected in the first plant section due to the influence of the recycled sludge. It is found that thecomputationally less expensive SRC method is applied outside its range of applicability with R2 =(0.3-0.6) < 0.7. Nevertheless, the ranking obtained with the SRC method for the influentialparameters is very similar to that of the Extended-FAST method, except for MLSS. However, toobtain reliable quantitative information on variance decomposition and to detect and quantify (insome cases considerable) interactions present among parameters the use of the computationallymore expensive Extended-FAST is found to be necessary in this case study.",
keywords = "MBR modelling, Wastewater treatment, global sensitivity analysis",
author = "Gaspare Viviani and Giorgio Mannina and Alida Cosenza",
year = "2011",
language = "English",
pages = "543--552",

}

TY - CONF

T1 - Global sensitivity analysis in ASM applications: comparison ofthe SRC and Extended-FAST method for a UCT-MBR model

AU - Viviani, Gaspare

AU - Mannina, Giorgio

AU - Cosenza, Alida

PY - 2011

Y1 - 2011

N2 - In this study global sensitivity analysis is performed to identify influential as well as non-influentialparameters in a model of a University Cape Town Membrane Bioreactor (UCT-MBR). Inparticular, the Standardised Regression Coefficients (SRC) and Extended-FAST sensitivity analysismethods are applied. The sensitivity of model variables towards parameter variation is analysed forCODTOT, SNH4, SNO3, SPO, and MLSS along five reactor compartments. Both methods indicate that theparameters identified as being influential differ from section to section due to the differentprocesses involved. Moreover, the relevant influence of the membrane filtration parameters isdetected in the first plant section due to the influence of the recycled sludge. It is found that thecomputationally less expensive SRC method is applied outside its range of applicability with R2 =(0.3-0.6) < 0.7. Nevertheless, the ranking obtained with the SRC method for the influentialparameters is very similar to that of the Extended-FAST method, except for MLSS. However, toobtain reliable quantitative information on variance decomposition and to detect and quantify (insome cases considerable) interactions present among parameters the use of the computationallymore expensive Extended-FAST is found to be necessary in this case study.

AB - In this study global sensitivity analysis is performed to identify influential as well as non-influentialparameters in a model of a University Cape Town Membrane Bioreactor (UCT-MBR). Inparticular, the Standardised Regression Coefficients (SRC) and Extended-FAST sensitivity analysismethods are applied. The sensitivity of model variables towards parameter variation is analysed forCODTOT, SNH4, SNO3, SPO, and MLSS along five reactor compartments. Both methods indicate that theparameters identified as being influential differ from section to section due to the differentprocesses involved. Moreover, the relevant influence of the membrane filtration parameters isdetected in the first plant section due to the influence of the recycled sludge. It is found that thecomputationally less expensive SRC method is applied outside its range of applicability with R2 =(0.3-0.6) < 0.7. Nevertheless, the ranking obtained with the SRC method for the influentialparameters is very similar to that of the Extended-FAST method, except for MLSS. However, toobtain reliable quantitative information on variance decomposition and to detect and quantify (insome cases considerable) interactions present among parameters the use of the computationallymore expensive Extended-FAST is found to be necessary in this case study.

KW - MBR modelling

KW - Wastewater treatment

KW - global sensitivity analysis

UR - http://hdl.handle.net/10447/57006

M3 - Other

SP - 543

EP - 552

ER -