Data-based methods for the screening and design of jet fuels

Abstract

To achieve climate neutrality in the aviation sector, research on new sustainable aviation fuels (SAF) is needed as the growing demand will exceed the production potential of established sustainable pathways. The focus is thereby not only on the exploration of sustainable feedstocks and the development of new production processes but also on the facilitation and acceleration of the whole fuel development process, from its conceptualization to its approval. The critical evaluation of a new production pathway guarantees the safe application and performance of a new fuel. The approval poses a major challenge for fuel producers, requiring a tremendous commitment of time, fuel volume and cost. Concepts that allow a fast-iterative, low-cost screening and design of new candidate fuels, to assess and optimize their chances for approval are thereby seen as key enablers. Established fuel screening concepts rely on model-based prediction, which, together with state- of-the-art compositional analytics, allow the fast assessment of SAF candidates from volumes as low as 5 mL. The design of new fuels, on the other hand, requires a comprehensive understanding of the composition of a jet fuel and properties considered critical for the fuel approval. This work describes the research and development of tools for the screening and design of jet fuels. Focusing on data-based methods, the tools are built from a database composed of both jet fuels and fuel components. It is thereby investigated whether and how data-based tools are able to support the screening and design of new SAF candidates and what their limitations are. For the jet fuel screening, three different modeling methods to predict physicochemical properties from compositional measurements are adapted and investigated: Direct correlation (DC), Mean Quantitative Structure-Property Relationship Modeling (M-QSPR) and Quantitative Structure-Property Relationship Modeling (QSPR) with sampling. All developed models are probabilistic, since the safety-relevant use case of jet fuel screening makes the consideration of uncertainties necessary. Rather than estimating one deterministic property value, probabilistic models estimate a distribution of values and with it the associated uncertainty. The predictive capabilities of the developed models are assessed using specially developed metrics and compared on the prediction of conventional and synthetic jet fuels. To put the developed models into reference, they are compared to established deterministic models from the literature. Identifying strengths and limitations of the different approaches, the models are applied to jet fuel screening to test theiradequacy for the assessment of new SAF candidates. To support the design of new SAF candidates, the relationships between the fuel composition and critical physicochemical properties are investigated. The relationships are investigated on the basis of fuel components and the influence of their chemical families as well as the structural aspects size and the branching. Trends and relations are characterized with graphs and quantitative metrics that illustrate correlation and state the average value for a change in composition. Both the developed models and design tools are applied to the use case of screening and then optimizing a real SAF candidate to maximize its chances for successful fuel approval. The SAF candidate and three optimized fuel variants with reformulated compositions are thereby screened to assess the most suitable production route. Afterwards, a blending analysis of the SAF candidate and the variants is conducted to estimate their maximum volume fraction in the mixture with representative conventional jet fuels, considering both the safety requirements as well as the potential reduction of CO2 and soot emissions. As potential next steps, this work identifies the need for advancements in the analytics of the fuel composition as well as the extension of the existing fuel property databases. The former would reduce the uncertainty in the property modeling, while the latter would increase both the predictive capability of the models and the understanding of the fuel property relations.