Homogeneous Sono-Fenton Process: Statistical Modeling and Global Sensitivity Analysis

AbstractThe main objective of this study is to obtain and validate a mathematical model to describe a complex homogeneous Sono-Fenton (HSF) process used for the removal of 4-chlorophenol model pollutant from aqueous effluents. The investigated process parameters (acoustic amplitude, power density depending on the surface of the tip, initial pollutant concentration and time) serve as input parameters for the statistical modeling, while the output parameters considered are the final pollutant concentration and energy delivered to the sample. The accuracy of the models is analyzed by the values of the determination coefficients and by graphical tools available in MATLAB software such as: the Kolmogorov–Smirnov test (KS test), the graphical sensitivity tools, e.g. contribution to the sample mean (CSM) and variance (CSV) plots. The robustness of the model is also analyzed by global sensitivity analysis. Furthermore, the optimum set of operating conditions are determined by using the nlintool function.

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Global Sensitivity Analysis of the Vibration Reduction System with Seated Human Body

Shock and Vibration ◽

10.1155/2018/3732746 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Igor Maciejewski ◽

Tomasz Krzyzynski

Keyword(s):

Sensitivity Analysis ◽

Vibration Isolation ◽

Global Sensitivity Analysis ◽

Operating Conditions ◽

Design Parameters ◽

Global Sensitivity ◽

Seat Suspension ◽

Sensitivity Indices ◽

Vibration Transmissibility ◽

Nonlinear Force

The paper deals with the global sensitivity analysis for the purpose of shaping the vibroisolation properties of suspension systems under strictly defined operating conditions. The variance-based method is used to evaluate an influence of nonlinear force characteristics on the system dynamics. The proposed sensitivity indices provide the basis for determining the effect of key design parameters on the vibration isolation performance. The vibration transmissibility behaviour of an exemplary seat suspension system is discussed in order to illustrate the developed methodology.

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A Comparison of Moment-Independent and Variance-Based Global Sensitivity Analysis Approaches for Wheat Yield Estimation with the Aquacrop-OS Model

Agronomy ◽

10.3390/agronomy10040607 ◽

2020 ◽

Vol 10 (4) ◽

pp. 607

Author(s):

Deepak Upreti ◽

Stefano Pignatti ◽

Simone Pascucci ◽

Massimo Tolomio ◽

Zhenhai Li ◽

...

Keyword(s):

Sensitivity Analysis ◽

Distribution Function ◽

Global Sensitivity Analysis ◽

Global Sensitivity ◽

Kolmogorov Smirnov ◽

Anderson Darling ◽

Parameter Approach ◽

Influential Parameters ◽

Smirnov Statistic ◽

Stress Parameters

The present work reports the global sensitivity analysis of the Aquacrop Open Source (AOS) model, which is the open-source version of the original Aquacrop model developed by the Food and Agriculture Organization (FAO). Analysis for identifying the most influential parameters was based on different strategies of global SA, density-based and variance-based, for the wheat crop in two different geographical locations and climates. The main objectives were to distinguish the model’s influential and non-influential parameters and to examine the yield output sensitivity. We compared two different methods of global sensitivity analysis: the most commonly used variance-based method, EFAST, and the moment independent density-based PAWN method developed in recent years. We have also identified non-influential parameters using Morris screening method, so to provide an idea of the use of non-influential parameters with a dummy parameter approach. For both the study areas (located in Italy and in China) and climates, a similar set of influential parameters was found, although with varying sensitivity. When compared with different probability distribution functions, the probability distribution function of yield was found to be best approximated by a Generalized Extreme Values distribution with Kolmogorov–Smirnov statistic of 0.030 and lowest Anderson–Darling statistic of 0.164, as compared to normal distribution function with Kolmogorov–Smirnov statistic of 0.122 and Anderson–Darling statistic of 4.099. This indicates that yield output is not normally distributed but has a rather skewed distribution function. In this case, a variance-based approach was not the best choice, and the density-based method performed better. The dummy parameter approach avoids to use a threshold as it is a subjective question; it advances the approach to setting up a threshold and gives an optimal way to set up a threshold and use it to distinguish between influential and non-influential parameters. The highly sensitive parameters to crop yield were specifically canopy and phenological development parameters, parameters that govern biomass/yield production and temperature stress parameters rather than root development and water stress parameters.

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Comparing global sensitivity analysis for a biofilm model for two-step nitrification using the qualitative screening method of Morris or the quantitative variance-based Fourier Amplitude Sensitivity Test (FAST)

Water Science & Technology ◽

10.2166/wst.2007.600 ◽

2007 ◽

Vol 56 (8) ◽

pp. 85-93 ◽

Cited By ~ 16

Author(s):

D. Brockmann ◽

E. Morgenroth

Keyword(s):

Sensitivity Analysis ◽

Global Sensitivity Analysis ◽

Screening Method ◽

Biofilm Reactor ◽

Operating Conditions ◽

Sensitivity Analyses ◽

Fast Method ◽

Biofilm Thickness ◽

Global Sensitivity ◽

Biofilm Model

Two different methods for global sensitivity analysis were compared exemplarily for a biofilm model for two-step nitrification. Especially for biofilm models, local sensitivity analysis is not very useful as parameters can vary over a large range. Parameters that were evaluated included kinetic and stoichiometric parameters, and also biofilm parameters, such as internal and external mass transfer, the biofilm thickness, and the biomass density. Global sensitivity analyses were performed for a range of operating conditions of a biofilm reactor. The results of the qualitative screening method of Morris were compared with the results of the quantitative variance-based method FAST regarding the input parameters indicated as unimportant. Both methods resulted in similar sets of parameters with a small influence on the model output, but the screening method of Morris required a much smaller number of model evaluations to compute the sensitivity measures than the FAST method.

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