Construction of a Robust Model for Prediction of Sustainable Aviation Fuel Properties based on the Chemical Composition


Supervisors: Dr. Xue Yong, Dr. Ehsan Alborzi
In collaboration with partners from Department of Chemistry, Department of Mechanical Engineering, and Sustainable Aviation Fuel-Innovation Center (SAF-IC) characterisation lab. 

This fully funded project presents an outstanding opportunity to conduct research on the development and understanding of Sustainable Aviation Fuel (SAF). SAF is derived from sustainable feedstocks such as captured CO2, waste oil and fats, green and municipal waste, and non-food crops. Its chemical composition closely resembles that of traditional fossil jet fuel. By utilizing SAF, significant reductions in carbon emissions throughout the entire life cycle of aviation fuel can be achieved, aligning with the goals of achieving net zero emissions compared to traditional jet fuel.

Given the progressive increase in production and application of SAF, it is crucial to establish a robust theoretical framework for predicting the fuel properties of SAF. Such a model would facilitate fuel pre-screening, offering fuel producers and jet engine manufacturers fast and quantitative information regarding the technical suitability of SAF. The primary objective of this project, therefore, is to develop a predictive model for fundamental fuel properties based on the chemical composition of the fuel. This predictive tool necessitates the construction of a comprehensive database comprising various properties of known surrogate fuels through experimental and theoretical works.

The successful PhD candidate for this project should possess a general understanding of aviation fuel properties and demonstrate a profound knowledge of analytical and organic chemistry. As a member of the research team within the Sustainable Aviation Fuel-Innovation Centre (SAF-IC), the candidate will collaborate with fellow researchers and academics to investigate and implement various numerical and experimental techniques pertinent to the project.

Specifically, the candidate will be involved in utilizing advanced tools such as two-dimensional comprehensive gas chromatography, as well as other relevant instruments designed for precise measurement of fuel properties. Additionally, the candidate will work with chemo informatics packages, including RD kit, molecular dynamics software, and graph neural network, to analyze and interpret data.

Interested candidates should email a covering letter and their Curriculum Vitae to Xue Yong ( or Ehsan Alborzi (

This studentship is funded by the Energy Institute at the University of Sheffield, and it is open to all students with UK residency.

For information and informal enquiries please contact:

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