Systematic evaluation of biofilm models for engineering practice: components and critical assumptions

2011 ◽  
Vol 64 (4) ◽  
pp. 930-944 ◽  
Author(s):  
J. P. Boltz ◽  
E. Morgenroth ◽  
D. Brockmann ◽  
C. Bott ◽  
W. J. Gellner ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Model Calibration ◽  
Biofilm Reactor ◽  
Systematic Evaluation ◽  
Engineering Practice ◽  
Model Parameters ◽  
Concentration Gradients ◽  
Reactor Model ◽  
Biofilm Model ◽  
Calibration Protocol

Biofilm models are valuable tools for the design and evaluation of biofilm-based processes despite several uncertainties including the dynamics and rate of biofilm detachment, concentration gradients external to the biofilm surface, and undefined biofilm reactor model calibration protocol. The present investigation serves to (1) systematically evaluate critical biofilm model assumptions and components and (2) conduct a sensitivity analysis with the aim of identifying parameter subsets for biofilm reactor model calibration. AQUASIM was used to describe submerged-completely mixed combined carbon oxidation and nitrification IFAS and MBBR systems, and tertiary nitrification and denitrification MBBRs. The influence of uncertainties in model parameters on relevant model outputs was determined for simulated scenarios by means of a local sensitivity analysis. To obtain reasonable simulation results for partially penetrated biofilms that accumulated a substantial thickness in the modelled biofilm reactor (e.g. 1,000 μm), an appropriate biofilm discretization was applied to properly model soluble substrate concentration gradients and, consistent with the assumed mechanism for describing biofilm biomass distribution, biofilm biomass spatial variability. The MTBL thickness had a significant impact on model results for each of the modelled reactor configurations. Further research is needed to develop a mathematical description (empirical or otherwise) of the MTBL thickness that is relevant to modern biofilm reactors. No simple recommendations for a generally applicable calibration protocol are provided, but sensitivity analysis has been proven to be a powerful tool for the identification of highly sensitive parameter subsets for biofilm (reactor) model calibration.

scholarly journals Biofilm carrier migration model describes reactor performance

2017 ◽  
Vol 75 (12) ◽  
pp. 2818-2828 ◽  
Author(s):  
Joshua P. Boltz ◽  
Bruce R. Johnson ◽  
Imre Takács ◽  
Glen T. Daigger ◽  
Eberhard Morgenroth ◽  
...  
Keyword(s):  
Plug Flow ◽  
Biofilm Reactor ◽  
Bulk Liquid ◽  
Reactor Performance ◽  
Reactor Model ◽  
Operational Conditions ◽  
Limit Model ◽  
Biofilm Model ◽  
Reactor Models ◽  
The Impact

The accuracy of a biofilm reactor model depends on the extent to which physical system conditions (particularly bulk-liquid hydrodynamics and their influence on biofilm dynamics) deviate from the ideal conditions upon which the model is based. It follows that an improved capacity to model a biofilm reactor does not necessarily rely on an improved biofilm model, but does rely on an improved mathematical description of the biofilm reactor and its components. Existing biofilm reactor models typically include a one-dimensional biofilm model, a process (biokinetic and stoichiometric) model, and a continuous flow stirred tank reactor (CFSTR) mass balance that [when organizing CFSTRs in series] creates a pseudo two-dimensional (2-D) model of bulk-liquid hydrodynamics approaching plug flow. In such a biofilm reactor model, the user-defined biofilm area is specified for each CFSTR; thereby, Xcarrier does not exit the boundaries of the CFSTR to which they are assigned or exchange boundaries with other CFSTRs in the series. The error introduced by this pseudo 2-D biofilm reactor modeling approach may adversely affect model results and limit model-user capacity to accurately calibrate a model. This paper presents a new sub-model that describes the migration of Xcarrier and associated biofilms, and evaluates the impact that Xcarrier migration and axial dispersion has on simulated system performance. Relevance of the new biofilm reactor model to engineering situations is discussed by applying it to known biofilm reactor types and operational conditions.

Sensitivity analysis and the challenges posed by multiple approaches: a multifaceted mess

2020 ◽  
Author(s):  
Monica Riva ◽  
Aronne Dell'Oca ◽  
Alberto Guadagnini
Keyword(s):  
Sensitivity Analysis ◽  
Model Calibration ◽  
Global Sensitivity Analysis ◽  
System Model ◽  
Model Parameters ◽  
Model Output ◽  
Multiple Model ◽  
Total Uncertainty ◽  
Global Sensitivity ◽  
Sensitivity Indices

<p>Modern models of environmental and industrial systems have reached a relatively high level of complexity. The latter aspect could hamper an unambiguous understanding of the functioning of a model, i.e., how it drives relationships and dependencies among inputs and outputs of interest. Sensitivity Analysis tools can be employed to examine this issue.</p><p>Global sensitivity analysis (GSA) approaches rest on the evaluation of sensitivity across the entire support within which system model parameters are supposed to vary. In this broad context, it is important to note that the definition of a sensitivity metric must be linked to the nature of the question(s) the GSA is meant to address. These include, for example: (i) which are the most important model parameters with respect to given model output(s)?; (ii) could we set some parameter(s) (thus assisting model calibration) at prescribed value(s) without significantly affecting model results?; (iii) at which space/time locations can one expect the highest sensitivity of model output(s) to model parameters and/or knowledge of which parameter(s) could be most beneficial for model calibration?</p><p>The variance-based Sobol’ Indices (e.g., Sobol, 2001) represent one of the most widespread GSA metrics, quantifying the average reduction in the variance of a model output stemming from knowledge of the input. Amongst other techniques, Dell’Oca et al. [2017] proposed a moment-based GSA approach which enables one to quantify the influence of uncertain model parameters on the (statistical) moments of a target model output.</p><p>Here, we embed in these sensitivity indices the effect of uncertainties both in the system model conceptualization and in the ensuing model(s) parameters. The study is grounded on the observation that physical processes and natural systems within which they take place are complex, rendering target state variables amenable to multiple interpretations and mathematical descriptions. As such, predictions and uncertainty analyses based on a single model formulation can result in statistical bias and possible misrepresentation of the total uncertainty, thus justifying the assessment of multiple model system conceptualizations. We then introduce copula-based sensitivity metrics which allow characterizing the global (with respect to the input) value of the sensitivity and the degree of variability (across the whole range of the input values) of the sensitivity for each value that the prescribed model output can possibly undertake, as driven by a governing model. In this sense, such an approach to sensitivity is global with respect to model input(s) and local with respect to model output, thus enabling one to discriminate the relevance of an input across the entire range of values of the modeling goal of interest. The methodology is demonstrated in the context of flow and reactive transport scenarios.</p><p> </p><p><strong>References</strong></p><p>Sobol, I. M., 2001. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Sim., 55, 271-280.</p><p>Dell’Oca, A., Riva, M., Guadagnini, A., 2017. Moment-based metrics for global sensitivity analysis of hydrological systems. Hydr. Earth Syst. Sci., 21, 6219-6234.</p>

scholarly journals Applicability of a distributed watershed pollution model in a data-poor environment in Santa Catarina State, Brazil

2008 ◽  
Vol 32 (4) ◽  
pp. 1699-1712 ◽  
Author(s):  
Ivan Luiz Zilli Bacic ◽  
David G. Rossiter ◽  
Christiaan Mathias Mannaerts
Keyword(s):  
Sensitivity Analysis ◽  
Model Calibration ◽  
Management Practices ◽  
Expert Knowledge ◽  
Stream Water ◽  
Model Parameters ◽  
Calibration Data ◽  
Santa Catarina ◽  
Environmental Models ◽  
Santa Catarina State

Intensification of agricultural production without a sound management and regulations can lead to severe environmental problems, as in Western Santa Catarina State, Brazil, where intensive swine production has caused large accumulations of manure and consequently water pollution. Natural resource scientists are asked by decision-makers for advice on management and regulatory decisions. Distributed environmental models are useful tools, since they can be used to explore consequences of various management practices. However, in many areas of the world, quantitative data for model calibration and validation are lacking. The data-intensive distributed environmental model AgNPS was applied in a data-poor environment, the upper catchment (2,520 ha) of the Ariranhazinho River, near the city of Seara, in Santa Catarina State. Steps included data preparation, cell size selection, sensitivity analysis, model calibration and application to different management scenarios. The model was calibrated based on a best guess for model parameters and on a pragmatic sensitivity analysis. The parameters were adjusted to match model outputs (runoff volume, peak runoff rate and sediment concentration) closely with the sparse observed data. A modelling grid cell resolution of 150 m adduced appropriate and computer-fit results. The rainfall runoff response of the AgNPS model was calibrated using three separate rainfall ranges (< 25, 25-60, > 60 mm). Predicted sediment concentrations were consistently six to ten times higher than observed, probably due to sediment trapping along vegetated channel banks. Predicted N and P concentrations in stream water ranged from just below to well above regulatory norms. Expert knowledge of the area, in addition to experience reported in the literature, was able to compensate in part for limited calibration data. Several scenarios (actual, recommended and excessive manure applications, and point source pollution from swine operations) could be compared by the model, using a relative ranking rather than quantitative predictions.

scholarly journals A framework for good biofilm reactor modeling practice (GBRMP)

2018 ◽  
Vol 77 (5) ◽  
pp. 1149-1164 ◽  
Author(s):  
Bruce E. Rittmann ◽  
Joshua P. Boltz ◽  
Doris Brockmann ◽  
Glen T. Daigger ◽  
Eberhard Morgenroth ◽  
...  
Keyword(s):  
Poor Performance ◽  
Specific Model ◽  
Biofilm Reactor ◽  
Model Parameters ◽  
Actual System ◽  
Reactor Modeling ◽  
Biofilm Model ◽  
Wide Range ◽  
Biofilm Modeling ◽  
Modeling Practice

Abstract A researcher or practitioner can employ a biofilm model to gain insight into what controls the performance of a biofilm process and for optimizing its performance. While a wide range of biofilm-modeling platforms is available, a good strategy is to choose the simplest model that includes sufficient components and processes to address the modeling goal. In most cases, a one-dimensional biofilm model provides the best balance, and good choices can range from hand-calculation analytical solutions, simple spreadsheets, and numerical-method platforms. What is missing today is clear guidance on how to apply a biofilm model to obtain accurate and meaningful results. Here, we present a five-step framework for good biofilm reactor modeling practice (GBRMP). The first four steps are (1) obtain information on the biofilm reactor system, (2) characterize the influent, (3) choose the plant and biofilm model, and (4) define the conversion processes. Each step demands that the model user understands the important components and processes in the system, one of the main benefits of doing biofilm modeling. The fifth step is to calibrate and validate the model: System-specific model parameters are adjusted within reasonable ranges so that model outputs match actual system performance. Calibration is not a simple ‘by the numbers’ process, and it requires that the modeler follows a logical hierarchy of steps. Calibration requires that the adjusted parameters remain within realistic ranges and that the calibration process be carried out in an iterative manner. Once each of steps 1 through 5 is completed satisfactorily, the calibrated model can be used for its intended purpose, such as optimizing performance, trouble-shooting poor performance, or gaining deeper understanding of what controls process performance.

scholarly journals Multi-site calibration, validation, and sensitivity analysis of the MIKE SHE Model for a large watershed in northern China

2012 ◽  
Vol 16 (12) ◽  
pp. 4621-4632 ◽  
Author(s):  
S. Wang ◽  
Z. Zhang ◽  
G. Sun ◽  
P. Strauss ◽  
J. Guo ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Variability ◽  
Model Calibration ◽  
Model Performance ◽  
Northern China ◽  
Hydrologic Model ◽  
Single Site ◽  
Mike She ◽  
The Third ◽  
Calibration Protocol

Abstract. Model calibration is essential for hydrologic modeling of large watersheds in a heterogeneous mountain environment. Little guidance is available for model calibration protocols for distributed models that aim at capturing the spatial variability of hydrologic processes. This study used the physically-based distributed hydrologic model, MIKE SHE, to contrast a lumped calibration protocol that used streamflow measured at one single watershed outlet to a multi-site calibration method which employed streamflow measurements at three stations within the large Chaohe River basin in northern China. Simulation results showed that the single-site calibrated model was able to sufficiently simulate the hydrographs for two of the three stations (Nash-Sutcliffe coefficient of 0.65–0.75, and correlation coefficient 0.81–0.87 during the testing period), but the model performed poorly for the third station (Nash-Sutcliffe coefficient only 0.44). Sensitivity analysis suggested that streamflow of upstream area of the watershed was dominated by slow groundwater, whilst streamflow of middle- and down- stream areas by relatively quick interflow. Therefore, a multi-site calibration protocol was deemed necessary. Due to the potential errors and uncertainties with respect to the representation of spatial variability, performance measures from the multi-site calibration protocol slightly decreased for two of the three stations, whereas it was improved greatly for the third station. We concluded that multi-site calibration protocol reached a compromise in term of model performance for the three stations, reasonably representing the hydrographs of all three stations with Nash-Sutcliffe coefficient ranging from 0.59–072. The multi-site calibration protocol applied in the analysis generally has advantages to the single site calibration protocol.

scholarly journals Automated calibration of advanced cyclic plasticity model parameters with sensitivity analysis for aluminium alloy 2024-T351

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401982998 ◽  
Author(s):  
Michal Peč ◽  
František Šebek ◽  
Josef Zapletal ◽  
Jindřich Petruška ◽  
Tasnim Hassan
Keyword(s):  
Sensitivity Analysis ◽  
Aluminium Alloy ◽  
Model Calibration ◽  
Kinematic Hardening ◽  
Material Model ◽  
Cyclic Plasticity ◽  
Model Parameters ◽  
Plasticity Model ◽  
Cyclic Plasticity Model

The plasticity models in finite element codes are often not able to describe the cyclic plasticity phenomena satisfactorily. Developing a user-defined material model is a demanding process, challenging especially for industry. Open-source Code_Aster is a rapidly expanding and evolving software, capable of overcoming the above-mentioned problem with material model implementation. In this article, Chaboche-type material model with kinematic hardening evolution rules and non-proportional as well as strain memory effects was studied through the calibration of the aluminium alloy 2024-T351. The sensitivity analysis was performed prior to the model calibration to find out whether all the material model parameters were important. The utilization of built-in routines allows the calibration of material constants without the necessity to write the optimization scripts, which is time consuming. Obtaining the parameters using the built-in routines is therefore easier and allows using the advanced modelling for practical use. Three sets of material model parameters were obtained using the built-in routines and results were compared to experiments. Quality of the calibration was highlighted and drawbacks were described. Usage of material model implemented in Code_Aster provided good simulations in a relatively simple way through the use of an advanced cyclic plasticity model via built-in auxiliary functions.

scholarly journals Parameters sensitivity analysis for a~crop growth model applied to winter wheat in the Huanghuaihai Plain in China

2014 ◽  
Vol 7 (3) ◽  
pp. 3867-3888 ◽  
Author(s):  
M. Liu ◽  
B. He ◽  
A. Lü ◽  
L. Zhou ◽  
J. Wu
Keyword(s):  
Sensitivity Analysis ◽  
Model Calibration ◽  
Regional Scale ◽  
Model Parameters ◽  
Sensitivity Index ◽  
Root Weight ◽  
Sensitivity Indices ◽  
Nutrition Supplement ◽  
Sources Of Variation ◽  
Crop Parameters

Abstract. Parameters sensitivity analysis is a crucial step in effective model calibration. It quantitatively apportions the variation of model output to different sources of variation, and identifies how "sensitive" a model is to changes in the values of model parameters. Through calibration of parameters that are sensitive to model outputs, parameter estimation becomes more efficient. Due to uncertainties associated with yield estimates in a regional assessment, field-based models that perform well at field scale are not accurate enough to model at regional scale. Conducting parameters sensitivity analysis at the regional scale and analyzing the differences of parameter sensitivity between stations would make model calibration and validation in different sub-regions more efficient. Further, it would benefit the model applied to the regional scale. Through simulating 2000 × 22 samples for 10 stations in the Huanghuaihai Plain, this study discovered that TB (Optimal temperature), HI (Normal harvest index), WA (Potential radiation use efficiency), BN2 (Normal fraction of N in crop biomass at mid-season) and RWPC1 (Fraction of root weight at emergency) are more sensitive than other parameters. Parameters that determine nutrition supplement and LAI development have higher global sensitivity indices than first-order indices. For spatial application, soil diversity is crucial because soil is responsible for crop parameters sensitivity index differences between sites.

scholarly journals Comparative hydrology: relationships among physical characteristics, hydrological behavior, and results of the SWAT model in different regions of Brazil (Hidrologia comparativa: relações entre características físicas, comportamento hidrológico e ....)

2015 ◽  
Vol 7 (6) ◽  
pp. 1187
Author(s):  
Jorge Enoch Furquim Werneck Lima ◽  
Suzana Montenegro ◽  
Abelardo Antônio De Assunção Montenegro ◽  
Sergio Koide
Keyword(s):  
Land Use ◽  
Sensitivity Analysis ◽  
Model Calibration ◽  
Swat Model ◽  
Physical Characteristics ◽  
Model Parameters ◽  
Caatinga Biome ◽  
Comparative Hydrology ◽  
The Relationship ◽  
Semi Arid

Comparative hydrology studies, either by the similarities or the differences in the obtained data and results, represent an important tool for advancing knowledge of cause-effect relationships between the physical characteristics of the basins and their hydrological behavior. The objective of this study was to present a comparative analysis of measured and simulated characteristics of experimental and representative basins in different regions of Brazil. The SWAT model was used. Four catchments were evaluated: Alto Ipanema, located in the Caatinga biome, with semi-arid climate; Tapacurá, in the transition zone between the Caatinga and Atlantic Forest biomes, with hot and humid tropical climate; and Lago Descoberto and Alto Jardim, both in the Cerrado biome and with tropical altitude climate. The catchments were compared with respect to their physical characteristics (climate, soil, altitude, and land use). Using sensitivity analysis, it was found which of the SWAT model parameters best explain the hydrological behavior of the study regions. Considering its characteristics, the parameters values obtained in each catchment after model calibration were analyzed and compared, indicating the possibility of using these values as reference for their regions. The results indicate a clear relationship between the physical characteristics of watersheds, their respective hydrological behavior, and the values of two SWAT model parameters, CN2 and SOL_K. For other parameters, the relationship between the obtained values do not reflected adequately the characteristics of the catchment, indicating a need for improvement in the physical basis of the calibrated model.

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)

2007 ◽  
Vol 56 (8) ◽  
pp. 85-93 ◽  
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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