Multi-objective optimization of equation of state molecular parameters: SAFT-VR Mie models for water

Graham, Edward; Forte, Esther; Burger, Jakob; Galindo, Amparo; Jackson, George; Adjiman, Claire S.

doi:10.1016/j.compchemeng.2022.108015

Cited by 5 publications

(5 citation statements)

References 78 publications

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“…However, the SAFT-VR Mie DBD model cannot predict the negative values of excess enthalpies in the water-rich region. To improve the results, it may be necessary to explore better SAFT-VR Mie models for water . The inclusion of multipolar interactions and the partial dissociation of the acid could also result in a better description of the data.…”

Section: Resultsmentioning

confidence: 99%

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Modeling of the Phase Behavior of Carboxylic Acid Systems Using the SAFT-VR Mie DBD Model: Application to the Simulation of the Acrylic Acid Production Process

Hafsi,

Paricaud

2024

Ind. Eng. Chem. Res.

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A new model, referred to as the SAFT-VR Mie DBD model, is proposed to capture the intricate behavior of short carboxylic acids. The new model is based on the SAFT-VR Mie model and integrates a general association term encompassing the formation of doubly bonded dimers (DBD), which enables precise predictions of vaporization enthalpies, densities, heat capacities, and phase behavior for both pure carboxylic acids and their mixtures. This work focuses on the acrylic acid (AA) production process from the oxidation of propene, which involves various unit operations (flash separation, absorption, liquid−liquid extraction, and distillation units). The SAFT-VR Mie DBD model can accurately describe vapor−liquid equilibrium (VLE) data, excess enthalpies, and other essential properties of mixtures containing acetic acid (ACE), acrylic acid (AA), diisopropyl ether (DIPE), water, and various components. To facilitate the practical application of the new thermodynamic model in an industrial context, a dynamic link library (DLL) is developed and made compatible with Simulis Thermodynamics to generate a CAPE-OPEN property package. The process simulations performed on Aspen Plus demonstrate the feasibility of using the SAFT-VR Mie DBD model for designing and optimizing the acrylic acid production process. This study serves as a proof of concept, thereby showcasing the possibility of employing complex thermodynamic models for simulating industrial processes.

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Section: Resultsmentioning

confidence: 99%

“…To improve the results, it may be necessary to explore better SAFT-VR Mie models for water. 44 The inclusion of multipolar interactions 41 and the partial dissociation of the acid could also result in a better description of the data.…”

Section: Mixturesmentioning

confidence: 99%

Modeling of the Phase Behavior of Carboxylic Acid Systems Using the SAFT-VR Mie DBD Model: Application to the Simulation of the Acrylic Acid Production Process

Hafsi,

Paricaud

2024

Ind. Eng. Chem. Res.

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“…When several local minima are present, instead of local solvers, solvers that search a larger parameter space have been used. 33,47,48 To explore a larger area of the parameter space and obtain a compromise between several properties, multiobjective optimization 31,33,49,50 has also been applied. Others have tried exploring the impact of adding more purecomponent thermodynamic data such as vaporization enthalpies, heat capacities, speeds of sound, and surface tensions to improve the quality of the fit.…”

Section: Introductionmentioning

confidence: 99%

“…Even for equations of state with only two parameters like the Soave–Redlich–Kwong equation of state, a certain degeneracy might be observed . In the case of SAFT-based equations of state, just for water, several publications exist in which this has been examined in detail to try to select the best set of parameters. − If one takes the usual average deviations of equations of state on pure-component densities and vapor pressures (0–2%) and the value of the objective function in the case of water from Clark et al, a very large range of values for ϵ assoc. and ϵ would lead to a similar model for the density and vapor pressure of water.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, several strategies have been suggested to try to mitigate the problem of parameter degeneracy. When several local minima are present, instead of local solvers, solvers that search a larger parameter space have been used. ,, To explore a larger area of the parameter space and obtain a compromise between several properties, multiobjective optimization ,,, has also been applied. Others have tried exploring the impact of adding more pure-component thermodynamic data such as vaporization enthalpies, heat capacities, speeds of sound, and surface tensions to improve the quality of the fit. ,− Another approach used also includes adding mixture data to correlate the pure-component parameters and discriminate between model parameter sets leading to models that predict pure-component and mixture phase equilibria with high accuracy. ,, For more information on what has been tried in the past to combat parameter degeneracy, we invite the reader to examine the analysis by Cripwell et al…”

Section: Introductionmentioning

confidence: 99%

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Confidence-Interval and Uncertainty-Propagation Analysis of SAFT-type Equations of State

Walker,

Mueller,

Smirnova

2023

J. Chem. Eng. Data

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Thermodynamic models and, in particular, Statistical Associating Fluid Theory (SAFT)-type equations, are vital in characterizing complex systems. This paper presents a framework for sampling parameter distributions in PC-SAFT and SAFT-VR Mie equations of state to examine parameter confidence intervals and correlations. Comparing the equations of state, we find that additional parameters introduced in the SAFT-VR Mie equation increase relative uncertainties (1%−2% to 3%−4%) and introduce more correlations. These correlations can be attributed to conserved quantities such as particle volume and interaction energy. When incorporating association through additional parameters, relative uncertainties increase further while slightly reducing correlations between parameters. We also investigate how uncertainties in parameters propagate to the predicted properties from these equations of state. While the uncertainties for the regressed properties remain small, when extrapolating to new properties, uncertainties can become significant. This is particularly true near the critical point where we observe that properties dependent on the isothermal compressibility observe massive divergences in the uncertainty. We find that these divergences are intrinsic to these equations of state and, as a result, will always be present regardless of how small the parameter uncertainties are.

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Prediction of Thermodynamic Properties and Phase Behaviour of CANDU Nuclear Reactor Fluid Coolant Using the SAFT-VR Mie Equation of State

Nicolaides,

Al-Wsaifer

2024

Lecture Notes in Mechanical Engineering

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Multi-objective optimization of equation of state molecular parameters: SAFT-VR Mie models for water

Cited by 5 publications

References 78 publications

Modeling of the Phase Behavior of Carboxylic Acid Systems Using the SAFT-VR Mie DBD Model: Application to the Simulation of the Acrylic Acid Production Process

Modeling of the Phase Behavior of Carboxylic Acid Systems Using the SAFT-VR Mie DBD Model: Application to the Simulation of the Acrylic Acid Production Process

Confidence-Interval and Uncertainty-Propagation Analysis of SAFT-type Equations of State

Prediction of Thermodynamic Properties and Phase Behaviour of CANDU Nuclear Reactor Fluid Coolant Using the SAFT-VR Mie Equation of State

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