Uncertainty analysis applied to thermodynamic models and process design – 1. Pure components

Hajipour, Samaneh; Satyro, Marco A.

doi:10.1016/j.fluid.2011.05.014

Cited by 31 publications

(44 citation statements)

References 32 publications

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“…These uncertainties then propagate through the process simulator to the process design at the end. An estimation of the model parameters and uncertainties associated with them can be performed by using the weighted least-square method with the minimization of an objective function based on the model, model parameters, experimental data and their uncertainties [33]. Linear or non-linear regression can be applied; according to the panel, the latter with a general non-linear form of each model for each property delivers better results.…”

Section: -For Model Developers: Model Developmentmentioning

confidence: 99%

“…What data are important?-After discussing data acquisition and assessment in general, the Round Table panel requested opinion of the process engineers in the audience, the knowledge of which properties are critical in the process design, i.e., which properties should be determined as precisely as possible. The general feeling of the audience was that the critical properties (T c , P c , V c ) need to be accurate, since they are used in predictions of thermodynamic and physical properties and also as input parameters in cubic equations of state [33] and therefore in process and reservoir engineering, design and optimization. Among phase equilibria, VLE is the most frequently faced in the process design.…”

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confidence: 99%

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Data quality and assessment, validation methods and error propagation through the simulation software: Report from the Round-Table Discussion at the 10th World Congress of Chemical Engineering in Barcelona (October 1–5, 2017)

2018

Chemical Engineering Research and Design

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The issues of data quality and propagation of data uncertainties into process design and plant specifications are of great current interest. Hence, two Working Parties of the European Federation of Chemical Engineers (EFCE) organized a Round Table Discussion on the topic, as part of the World Congress of Chemical Engineering (WCCE10) in Barcelona, in October 2017. The discussion was guided by industrial and academic experts, with the audience as a key part of the discussion, trying to find some answers in three areas: Data acquisition and evaluation of experimental uncertainties, tools for data reconciliation to improve their quality, and impact of data uncertainties on the process at the end. Several concrete stories are presented that demonstrate the importance of considering data quality and all possible contributions to the uncertainty of chemical process design. Difficulties associated with data quality are discussed at various levels: (1) the experimentalists (measurement issues, evaluation of uncertainties, use of consistency analysis tools); (2) model developers (capture of adequate physics, parameter regression strategies, uncertainty propagation), (3) vendors of process simulation software, and (4) process engineers (who are responsible at the end). Paths for improvements were proposed through better and more efficient communication among different participants, as well as through education.

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Section: -For Model Developers: Model Developmentmentioning

confidence: 99%

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confidence: 99%

Data quality and assessment, validation methods and error propagation through the simulation software: Report from the Round-Table Discussion at the 10th World Congress of Chemical Engineering in Barcelona (October 1–5, 2017)

2018

Chemical Engineering Research and Design

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“…[13] and references cited therein) or stochastically, e.g. by using statistical inference methodologies [14,15]. In a previous work [16], the present authors used aerodynamic data instead of thermodynamic ones for calibrating the material-dependent coefficients of equations of state (EOS) used to model the thermodynamic behavior of the working fluid.…”

Section: Introductionmentioning

confidence: 99%

Robust prediction of dense gas flows under uncertain thermodynamic models

Merle

Cinnella

2019

Reliability Engineering & System Safety

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A Bayesian approach is developed to quantify uncertainties associated with the thermodynamic models used for the simulation of dense gas flows, i.e. flows of gases characterized by complex molecules of moderate to high molecular weight, in thermodynamic conditions of the general order of magnitude of the liquid/vapor critical point. The thermodynamic behaviour of dense gases can be modelled through equations of state with various mathematical structures, all involving a set of material-dependent coefficients. For several organic fluids of industrial interest abundant and high-quality thermodynamic data required to specify such coefficients are hardly available, leading to undetermined levels of uncertainty of the equation output. Additionally, the best choice for the kind of equation of state (mathematical form) to be used is not always easy to determine and it is often based on expert opinion. In other terms, equations of state introduce both parametric and model-form uncertainties, which need to be quantified to make reliable predictions of the flow field. In this paper we propose a statistical inference methodology for estimating both kinds of uncertainties simultaneously. Our approach consists of a calibration step and a prediction step. The former allows to infer on the parameters to be input to the equation of state, based on the observation of aerodynamic quantities like pressure measurements at some locations in the dense gas flow. The subsequent prediction step allows to predict unobserved flow configurations based on the inferred posterior distributions of the coefficients. Model-form uncertainties are incorporated in the prediction step by using a Bayesian model averaging (BMA) approach. This consists in constructing an average of the predictions of various competing models weighted by the posterior model probabilities. Bayesian averaging also provides a useful tool for making robust predictions from a set of alternative calibration scenarios (Bayesian model-scenario averaging or BMSA). The proposed methodology is assessed for a class of dense gas flows, namely transonic flows around an isolated airfoil, at various free-stream thermodynamic conditions in the dense-gas region.

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“…More recently Mathias and 20 co-workers [17][18][19] also investigated the importance of uncertainty propagation for processes such as CO 2 capture. Hajipour and co-workers [20][21][22] Most research has focused on the propagated error from a thermodynamic model to various unit operations such as distillation columns (e.g. [5,6,17,23]).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of the CPA and a quadrupolar CPA equation of state – With emphasis on CO2

Bjørner

Sin

Kontogeorgis

2016

Fluid Phase Equilibria

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The parameters of thermodynamic models, such as the cubic plus association (CPA) equation of state, are subject to uncertainties due to measurement errors in the experimental data that the models are correlated to. More importantly as the number of adjustable parameters increase, the parameter estimation problem becomes more complicated due to parameter identifiability issues. In this work the uncertainties in the pure compound parameters of CO 2 are investigated using several different CPA approaches, including a new quadrupolar CPA. The uncertainties are estimated using both least squares estimation and the bootstrap method for parameter estimation. The uncertainties in the parameters estimated from the bootstrap method are propagated to physical property and vapor liquid equilibrium predictions using Monte Carlo simulations.The results indicate that both the pure compound parameter uncertainty and the propagated uncertainty is negligible for the modeling approaches which employ three adjustable parameters. For modeling approaches with more than three adjustable parameters, however, there may be significant uncertainties in the pure compound parameters, as well as a high degree of correlation between the adjustable parameters. This results in significant propagated errors * Corresponding author Email address: mgabj@kt.dtu.dk (Martin G. Bjørner)Preprint submitted to Fluid Phase Equilibria December 31, 2015for certain output properties. To reduce the uncertainty in the adjustable model parameters the heat of vaporization was included as additional correlation data.This resulted in parameter distributions which followed a normal distribution more closely, however, the correlation between the adjustable parameters remained high. Overall the results indicate, that it is important to report parameter uncertainties together with their correlation matrix when a model is developed, so that better informed decisions can be made, for instance about which model extension, or association scheme should be employed.

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Uncertainty analysis applied to thermodynamic models and process design – 1. Pure components

Cited by 31 publications

References 32 publications

Data quality and assessment, validation methods and error propagation through the simulation software: Report from the Round-Table Discussion at the 10th World Congress of Chemical Engineering in Barcelona (October 1–5, 2017)

Data quality and assessment, validation methods and error propagation through the simulation software: Report from the Round-Table Discussion at the 10th World Congress of Chemical Engineering in Barcelona (October 1–5, 2017)

Robust prediction of dense gas flows under uncertain thermodynamic models

Uncertainty analysis of the CPA and a quadrupolar CPA equation of state – With emphasis on CO2

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