Practical identifiability of ASM2d parameters—systematic selection and tuning of parameter subsets

ABSTRACT:A rigorous methodology is developed that addresses numerical and statistical issues when developing group contribution (GC) based property models such as regression methods, optimization algorithms, performance statistics, outlier treatment, parameter identifiability and uncertainty of the prediction. The methodology is evaluated through development of a GC method for prediction of the heat of combustion ( ) for pure components. The results showed that robust regression lead to best performance statistics for parameter estimation. Bootstrap method is found a valid alternative to calculate parameter estimation errors when underlying distribution of residuals is unknown. Many parameters (first, second, third order groups contributions) are found unidentifiable from the typically available data, with large estimation error bounds and significant correlation. Due to this poor parameter 2 identifiability issues, reporting of the 95%-confidence intervals of the predicted property values should be mandatory as opposed to reporting only single value prediction, currently the norm in literature. Moreover, inclusion of higher order groups (additional parameters) does not always lead to improved prediction accuracy for the GC-models, in some cases it may even increase the prediction error (hence worse prediction accuracy). However, additional parameters do not affect calculated 95%-confidence interval. Last but not least, the newly developed GC model of the heat of combustion ( ) shows predictions of great accuracy and quality (the most data falling within the 95% confidence intervals) and provides additional information on the uncertainty of each prediction compared to other models reported in literature.

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“…less than 0.7 43,44 , and b) Parameter estimation errors (i.e. 95% confidence intervals) should be sufficiently low 45 .…”

Section: Parameter Identifiability Parameter Identifiability Is a Comentioning

confidence: 99%

A Comprehensive Methodology for Development, Parameter Estimation, and Uncertainty Analysis of Group Contribution Based Property Models—An Application to the Heat of Combustion

et al. 2015

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“…The division of organic substances in the ASM2 model is much more complex than in the ASM1 version, because it takes into account 19 constituents used in wastewater and active sludge characterisation. Ten of them relate to insoluble constituents, nine to soluble ones [2,6,9,10]. The constituents of total COD according to the ASM1 and ASM2 models are presented respectively by the equations (2.1) and (2.2): When the biomass fraction is not included, both equations are simplified to the form CODtot = SS+SI+XSXI, g/m 3 .…”

Section: Cod Fraction In Biokinetic Asm Modelsmentioning

confidence: 99%

Cod Fractions In Mechanical-Biological Wastewater Treatment Plant

Płuciennik-Koropczuk

Jakubaszek

Myszograj

et al. 2017

Civil and Environmental Engineering Reports

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The paper presents results of studies concerning the designation of COD fraction in the raw, mechanically treated and biologically treated wastewater. The test object was a wastewater treatment plant with the output of over 20,000 PE. The results were compared with data received in the ASM models. During investigation following fractions of COD were determined: dissolved non-biodegradable SI, dissolved easily biodegradable SS, in organic suspension slowly degradable XS and in organic suspension nonbiodegradable XI. Methodology for determining the COD fraction was based on the guidelines ATV-A 131. The real percentage of each fraction in total COD in raw wastewater are different from data received in ASM models.

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“…GSA methods evaluate the influence of model factors on the model output variance (such as the methods described in e.g. Chan et al, 2004, Saltelli et al (2006, Gatelli et al, (2009) Saltelli and Annoni (2010)) or on the sensitivity function (relating the output changes with the variation in the factors), as described in the examples provided for example by Reichert and Vanrolleghem (2001), Brun et al (2002) and Freni et al (2009b). While the variance based approaches ranks the model factors according to their influence on the output without requiring any field observations, the latter approaches (also defined as identifiability analysis) extends the sensitivity analysis by identifying the factors that can be estimated by using the available measurements (as in the example presented by Freni et al, 2011).…”

Section: Identification Of Sensitive Factors In the Dynamic Modelmentioning

confidence: 99%

Modelling the fate of organic micropollutants in stormwater ponds

Vezzaro

Eriksson

Ledin

et al. 2011

Science of The Total Environment

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Urban water managers need to estimate the potential removal of organic micropollutants (MP) in stormwater treatment systems to support MP pollution control strategies. This study documents how the potential removal of organic MP in stormwater treatment systems can be quantified by using multimedia models. The fate of four different MP in a stormwater retention pond was simulated by applying two steady-state multimedia fate models (EPI Suite and SimpleBox) commonly applied in chemical risk assessment and a dynamic multimedia fate model (Stormwater Treatment Unit Model for Micro Pollutants -STUMP). The four simulated organic stormwater MP (iodopropynyl butylcarbamate -IPBC, benzene, glyphosate and pyrene) were selected according to their different urban sources and environmental fate. This ensures that the results can be extended to other relevant stormwater pollutants. All three models use substance inherent properties to calculate MP fate but differ in their ability to represent the small physical scale and high temporal variability of stormwater treatment systems. Therefore the three models generate different results. A Global Sensitivity Analysis (GSA) highlighted that settling/resuspension of particulate matter was the most sensitive process for the dynamic model. The uncertainty of the estimated MP fluxes can be reduced by calibrating the dynamic model against total suspended solids data. This reduction in uncertainty was more significant for the substances with strong tendency to sorb, i.e. glyphosate and pyrene and less significant for substances with a smaller tendency to sorb, i.e. IPBC and benzene. The results provide support to the elaboration of MP pollution control strategies by limiting the need for extensive and complex monitoring campaigns targeting the wide range of specific organic MP found in stormwater runoff.Please cite the published version of this article as: . Modelling the fate of organic micropollutants in stormwater ponds.

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Practical identifiability of ASM2d parameters—systematic selection and tuning of parameter subsets

Cited by 314 publications

References 13 publications

A Comprehensive Methodology for Development, Parameter Estimation, and Uncertainty Analysis of Group Contribution Based Property Models—An Application to the Heat of Combustion

A Comprehensive Methodology for Development, Parameter Estimation, and Uncertainty Analysis of Group Contribution Based Property Models—An Application to the Heat of Combustion

Cod Fractions In Mechanical-Biological Wastewater Treatment Plant

Modelling the fate of organic micropollutants in stormwater ponds

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