Maximizing Sustainable aviation fuel usage through optimization of distillation cut points and blending

Yang, Zhibin; Boehm, Randall C.; Bell, David C.; Heyne, Joshua S.

doi:10.1016/j.fuel.2023.129136

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…The distillation curve of jet fuel correlates with a multitude of other jet fuel properties . For example, the front end of the distillation curve determines its flash point, and the back end of the curve strongly impacts its fluidity characteristics . Five test methods for distillation curve measurement are referenced in the jet fuel specification ASTM D1655, where D86 is the referee method and other alternative methods are D2887/IP 406, D7344, D7345, and IP 123.…”

Section: Introductionmentioning

confidence: 99%

Volatility Measurements of Sustainable Aviation Fuels: A Comparative Study of D86 and D2887 Methods

Yang,

Bell,

Cronin

et al. 2024

ACS Sustainable Chem. Eng.

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The volatility and evaporation rate of aviation fuels impact combustion efficiency at some operating conditions as well as the jet-engine combustor operability, including ignition, lean blowout, and combustion dynamics. To help characterize the volatility of jet fuel, one experimental approach utilizes a one-plate atmospheric distillation (ASTM D86), while the other employs a close to infinite plate system in a gas chromatogram (GC) and the corresponding elution time of n-alkanes to simulate a distillation curve (ASTM D2887). The simulated distillation has been more repeatable historically, but with the advent of sustainable aviation fuels, the interpretation of this GC data and its correlation to the traditional one-plate approach are not clear. Here, we measured the one-plate (D86) and simulated (ASTM D2887) distillations of neat SAF candidates, their blends, and several conventional fuels. A total of 66 and 4 samples were measured in the simulated distillation and one-plate experiments, respectively. A simulated distillation curve blend rule is reported here as well as the impact of blending high concentrations of single components in HEFA. Significant disagreements between the calculation of D86 correlated data from D2887 data and the directly measured D86 data are discussed.

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Section: Introductionmentioning

confidence: 99%

Volatility Measurements of Sustainable Aviation Fuels: A Comparative Study of D86 and D2887 Methods

Yang,

Bell,

Cronin

et al. 2024

ACS Sustainable Chem. Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

Perspective on Fuel Property Blending Rules for Design and Qualification of Aviation Fuels: A Review

Boehm,

Yang,

Bell

et al. 2024

Energy Fuels

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Supply Chain Optimization in Industry 5.0: An Experimental Investigation Using Al

Ivanovich Vatin,

John,

Nangia

et al. 2024

BIO Web Conf.

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This experimental study examines the use of AI-driven supply chain management solutions in the framework of Industry 5.0. An analysis of fictitious data that represented product inventory, supplier details, customer orders, and transportation details showed significant cost savings in transportation logistics (10%), improvements in supplier cost efficiency (20%), and significant reductions in excess inventory (10%). In Industry 5.0, artificial intelligence (AI) emerges as a key technology that can promote effective, customer-focused, and sustainable supply chains.

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Maximizing Sustainable aviation fuel usage through optimization of distillation cut points and blending

Cited by 5 publications

References 21 publications

Volatility Measurements of Sustainable Aviation Fuels: A Comparative Study of D86 and D2887 Methods

Volatility Measurements of Sustainable Aviation Fuels: A Comparative Study of D86 and D2887 Methods

Perspective on Fuel Property Blending Rules for Design and Qualification of Aviation Fuels: A Review

Supply Chain Optimization in Industry 5.0: An Experimental Investigation Using Al

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