Surrogate generation and evaluation of gasolines

Grubinger, Tobias; Lenk, G; Schubert, Nikolai; Wallek, Thomas

doi:10.1016/j.fuel.2020.118642

Cited by 6 publications

(10 citation statements)

References 35 publications

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“…The algorithm used for the calculation of the surrogate composition was initially developed by Reiter et al for the generation of surrogates for crude oil, diesel, biodiesel, and mixtures thereof. − A subsequent adaptation by Grubinger et al focused on gasoline surrogates . Preliminary investigations revealed that surrogates with a focus on boiling curve, density, octane number, and molar mass do not depict the extractive properties in the context of liquid–liquid equilibria with sufficient accuracy, which will be illustrated in Subsection .…”

Section: Algorithm For Surrogate Optimizationmentioning

confidence: 99%

“…Despite its simplicity, the model provided satisfactory results for predicting the cetane number of diesel , and kerosene surrogates . RON numbers for the starting basis of 30 components were taken from Grubinger et al, while the RON numbers for the additional 10 aromatic components were estimated by the method proposed by Albahri …”

Section: Algorithm For Surrogate Optimizationmentioning

confidence: 99%

“… 18 − 20 A subsequent adaptation by Grubinger et al focused on gasoline surrogates. 16 Preliminary investigations revealed that surrogates with a focus on boiling curve, density, octane number, and molar mass do not depict the extractive properties in the context of liquid–liquid equilibria with sufficient accuracy, which will be illustrated in Subsection 4.2 . To overcome this, experimental liquid–liquid equilibrium data in the form of distribution coefficients had to be implemented as an additional criterion for the optimization procedure.…”

Section: Algorithm For Surrogate Optimizationmentioning

confidence: 99%

“…Examples of such key properties are standardized ratings like distillation characteristics, octane number, and density, or nonstandardized values like the C/H ratio, viscosity, and heating value. Once the target properties are defined, the composition of the surrogate is calculated by an algorithm which is designed to minimize an objective function that accounts for deviations between the given properties of the real fuel and the calculated properties of the surrogate …”

Section: Introductionmentioning

confidence: 99%

“…Once the target properties are defined, the composition of the surrogate is calculated by an algorithm which is designed to minimize an objective function that accounts for deviations between the given properties of the real fuel and the calculated properties of the surrogate. 16 …”

Section: Introductionmentioning

confidence: 99%

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Surrogates for Liquid–Liquid Extraction

Neubauer,

Lenk,

Schubert

et al. 2023

ACS Omega

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Fuel surrogates are mixtures that mimic the properties of real fuels with only a small number of components, simplifying the calculation and simulation of fuel-related processes. This work extends a previously published surrogate optimization algorithm toward the generation of fuel surrogates with a focus on liquid–liquid extraction characteristics. For this purpose, experimental liquid–liquid equilibrium data from batch extraction experiments are incorporated into the calculation procedure as an additional constraint. The use of the method is demonstrated by optimizing a surrogate for the catalytic reformate. Application of the surrogate to an extraction process and comparison with experimental data demonstrate that the resulting surrogate accurately depicts the properties of the real mixture with regard to liquid–liquid extraction performance. This demonstrates that the use of such surrogates is of particular interest for mixtures used as extracting agents for biofuels.

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