KAUST DepartmentChemical Engineering Program
Physical Science and Engineering (PSE) Division
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Biological and Environmental Science and Engineering (BESE) Division
Clean Combustion Research Center
Visual Computing Center (VCC)
Permanent link to this recordhttp://hdl.handle.net/10754/681565
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AbstractHigh-performance fuel design is imperative to achieve cleaner burning and high-efficiency engine systems. We introduce a data-driven artificial intelligence (AI) framework to design liquid fuels exhibiting tailor-made properties for combustion engine applications to improve efficiency and lower carbon emissions. The fuel design approach is a constrained optimization task integrating two parts: (i) a deep learning (DL) model to predict the properties of pure components and mixtures and (ii) search algorithms to efficiently navigate in the chemical space. Our approach presents the mixture-hidden vector as a linear combination of each single component’s vectors in each blend and incorporates it into the network architecture (the mixing operator (MO)). We demonstrate that the DL model exhibits similar accuracy as competing computational techniques in predicting the properties for pure components, while the search tool can generate multiple candidate fuel mixtures. The integrated framework was evaluated to showcase the design of high-octane and low-sooting tendency fuel that is subject to gasoline specification constraints. This AI fuel design methodology enables rapidly developing fuel formulations to optimize engine efficiency and lower emissions.
CitationKuzhagaliyeva, N., Horváth, S., Williams, J., Nicolle, A., & Sarathy, S. M. (2022). Artificial intelligence-driven design of fuel mixtures. Communications Chemistry, 5(1). https://doi.org/10.1038/s42004-022-00722-3
SponsorsThis paper is based on work supported by the Saudi Aramco Research and Development Center FUELCOM3 Program under Master Research Agreement Number 6600024505/01. FUELCOM (Fuel Combustion for Advanced Engines) is a collaborative research undertaking between Saudi Aramco and KAUST, intended to address the fundamental aspects of hydrocarbon fuel combustion in engines, and develop fuel/engine design tools suitable for advanced combustion modes.
PublisherSpringer Science and Business Media LLC
CollectionsArticles; Biological and Environmental Science and Engineering (BESE) Division; Physical Science and Engineering (PSE) Division; Chemical Engineering Program; Clean Combustion Research Center; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Except where otherwise noted, this item's license is described as Archived with thanks to Communications Chemistry under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0