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Artificial intelligence-driven design of fuel mixtures
Communications Chemistry
  • Nursulu Kuzhagaliyeva, Clean Combustion Research Center (CCRC), Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
  • Samuel Horváth, Visual Computing Center (VCC), Computer, Electrical and Mathematical Sciences & Engineering Division, KAUST, Thuwal, 23955-6900, Saudi Arabia & Mohamed bin Zayed University of Artificial Intelligence
  • John Williams, Aramco Fuel Research Center, 232 Avenue Bonaparte, Rueil-Malmaison, 92852, France
  • Andre Nicolle, Aramco Fuel Research Center, 232 Avenue Bonaparte, Rueil-Malmaison, 92852, France
  • S. Mani Sarathy, Clean Combustion Research Center (CCRC), Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
Document Type
Article
Abstract

High-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. © 2022, The Author(s).

DOI
10.1038/s42004-022-00722-3
Publication Date
9-16-2022
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Uploaded: 05 October 2022

Citation Information
N. Kuzhagaliyeva, S. Horváth, J. Williams, A. Nicolle and S.M.Sarathy, "Artificial intelligence-driven design of fuel mixtures", Communication Chemistry, Vol 5 (111), Sept 2022, doi: 10.1038/s42004-022-00722-3