Uncertainty analysis in WWTP model applications: A critical discussion using an example from design

Sin, Gürkan; Gernaey, Krist V.; Neumann, Marc B.; Loosdrecht, M.C.M. van; Gujer, Willi

doi:10.1016/j.watres.2009.03.048

Cited by 139 publications

(107 citation statements)

References 18 publications

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“…Even though providing significant information for relative comparisons, since CF , are based on simplifications of more complex models, they are not fully trustworthy for absolute comparisons because they carry substantial uncertainty. Hence, for the characterisation of uncertainties in CF , , the expert review method was used, a commonly used approach for uncertainty analysis in engineering studies [34], [37]. Thus, three classes of uncertainties for CFs were defined: the class 1 uncertainty refers to 25% variation, the class 2 refers to 50% variation, and the class 3 refers to 75% variation around the reported (mean) values of CFs.…”

Section: Step 4: Monte Carlo Techniquementioning

confidence: 99%

“…The Latin Hypercube Sampling (LHS) is used to sample from the parameter space [37] defined in the previous step, where the user needs to, a priori, specify the total sample number, N. After the sampling, one obtains a sampling matrix with N rows and p columns, where N is the total number of samples and p refers to the number of uncertain parameters for which the sampling is performed.…”

Section: Latin Hypercube Sampling and Monte Carlo Simulationsmentioning

confidence: 99%

“…Through Latin hypercube sampling with correlation structure/control [34], [37] 500 future scenarios (realizations of uncertainty) were generated for each of the input parameters from the input uncertainty domain.…”

Section: Step 42: Lhs and Monte Carlo Simulationsmentioning

confidence: 99%

See 2 more Smart Citations

A framework for techno-economic & environmental sustainability analysis by risk assessment for conceptual process evaluation

Gargalo

Carvalho

Gernaey

et al. 2016

Biochemical Engineering Journal

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The need to achieve a sustainable process performance has become increasingly important in order to keep a competitive advantage in the global markets. Development of comprehensive and systematic methods to accomplish this goal is the subject of this work. To this end, a multi-level framework for techno-economic and environmental sustainability analysis through risk assessment is proposed for the early-stage design and screening of conceptual process alternatives. The alternatives within the design space are analyzed following the framework's work-flow, which targets the following: (i) quantify the economic risk; (ii) perform the monetary valuation of environmental impact categories under uncertainty; (iii) quantify the potential environmental risk; (iv) measure the alternatives' eco-efficiency identifying possible trade-offs; and, lastly (v) propose a joint risk assessment matrix for the quantitative and qualitative assessment of sustainability at the decision-support level. Through the application of appropriate methods in a hierarchical manner, this tool leads to the identification of the potentially best and more sustainable solutions.2 Furthermore, the application of the framework is highlighted by screening two conceptual glycerol bioconversion routes to value-added chemicals namely 1,3-propanediol (1,3-PDO) and succinic acid.

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Section: Step 4: Monte Carlo Techniquementioning

confidence: 99%

Section: Latin Hypercube Sampling and Monte Carlo Simulationsmentioning

confidence: 99%

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A framework for techno-economic & environmental sustainability analysis by risk assessment for conceptual process evaluation

Gargalo

Carvalho

Gernaey

et al. 2016

Biochemical Engineering Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore the framework helps paving the way for risk based decision making. From this point of view, the results of uncertainty and sensitivity analysis provide a quantitative basis to justify safety factors, as well as support better informed decision making thereby contributing to cost-savings in engineering projects as demonstrated elsewhere (Sin, Gernaey, Neumann, van Loosdrecht, & Gujer, 2009). …”

Section: Discussionmentioning

confidence: 94%

A framework for model-based optimization of bioprocesses under uncertainty: Lignocellulosic ethanol production case

Morales-Rodríguez

Meyer

Gernaey

et al. 2012

Computers & Chemical Engineering

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Abstract:This study presents the development and application of a systematic model-based framework for bioprocess optimization. The framework relies on the identification of sources of uncertainties via global sensitivity analysis, followed by the quantification of their impact on performance evaluation metrics via uncertainty analysis. Finally, stochastic programming is applied to drive the process development efforts forward subject to these uncertainties. The framework is evaluated on four different process configurations for cellulosic ethanol production including Simultaneous Further stochastic optimization demonstrated the options for further reduction of the production costs with different processing configurations, reaching a reduction of up to 28% in the production cost in the SHCF configuration compared to the base case operation. Further, the framework evaluated here for uncertainties in the technical domain, can also be used to evaluate the impact of market uncertainties (feedstock prices, selling price of ethanol, etc) and political uncertainties (such as subsidies) on the economic feasibility of lignocellulosic ethanol production.

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“…Commonly, Monte Carlo simulations over a pre-assumed range of parameters [18] [19] [20], the autoregressive moving average for uncertainty analysis associated with the time series [21] [22] [23], the generalized likelihood uncertainty estimation (GLUE) method [24], and the Bayesian approach [25] [26] [27] [28] are used uncertainty quantification. Albrecht [29] Parameter estimation and uncertainty analysis by using Bayesian approach are widely used in different fields of science.…”

Section: S S H Boosari Et Almentioning

confidence: 99%

Application of Bayesian Approach in the Parameter Estimation of Continuous Lumping Kinetic Model of Hydrocracking Process

Boosari¹,

Makouei²,

Stewart³

2017

ACES

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Hydrocracking is a catalytic reaction process in the petroleum refineries for converting the higher boiling temperature residue of crude oil into a lighter fraction of hydrocarbons such as gasoline and diesel. In this study, a modified continuous lumping kinetic approach is applied to model the hydrocracking of vacuum gas oil. The model is modified to take into consideration the reactor temperature on the reaction yield distribution. The model is calibrated by maximizing the likelihood function between the modeled and measured data at four different reactor temperatures. Bayesian approach parameter estimation is also applied to obtain the confidence interval of model parameters by considering the uncertainty associated with the measured errors and the model structural errors. Then Monte Carlo simulation is applied to the posterior range of the model parameters to obtain the 95% confidence interval of the model outputs for each individual fraction of the hydrocracking products. A good agreement is observed between the output of the calibrated model and the measured data points. The Bayesian approach based on the Markov Chain Monte Carlo simulation is shown to be efficient to quantify the uncertainty associated with the parameter values of the continuous lumping model.

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Uncertainty analysis in WWTP model applications: A critical discussion using an example from design

Cited by 139 publications

References 18 publications

A framework for techno-economic & environmental sustainability analysis by risk assessment for conceptual process evaluation

A framework for techno-economic & environmental sustainability analysis by risk assessment for conceptual process evaluation

A framework for model-based optimization of bioprocesses under uncertainty: Lignocellulosic ethanol production case

Application of Bayesian Approach in the Parameter Estimation of Continuous Lumping Kinetic Model of Hydrocracking Process

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