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A world-renowned research center pursuing leading solutions to global challenges arising from the combustion of fossil fuels, such as pollutant control, global warming and climate change abatement, and sustainable fuel usage.

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The Clean Combustion Research Center (CCRC) collaborates on research projects with several world-renowned industries. CCRC's world-class faculty, researchers, and students help companies solve their most challenging problems.



04 January, 2023

Laser imaging helps clean fuels live up to their reputation

A technique that enables KAUST researchers to identify two kinds of pollutants with a single laser beam could make it easier to generate heat and power from ammonia, a carbon-free hydrogen carrier.

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03 January, 2023

Professor Aamir Farooq named a Fellow of the Royal Society of Chemistry

King Abdullah University of Science and Technology (KAUST) Professor of Mechanical Engineering Aamir Farooq has recently been appointed a Fellow of the Royal Society of Chemistry (FRSC). Professor Farooq, chair of the KAUST Mechanical Engineering Program, was elected for his contributions to chemical kinetics research.

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01 November, 2022

AI screens to make transport fuels green

An inverse mixture-design approach based on machine learning can teach computers to create mixtures from a set of target properties. Developed by KAUST, this could help find high-performance transport fuels that burn efficiently while releasing little carbon dioxide (CO2) into the atmosphere.

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KAUST Research Conference | AI for Energy, 2023

Delegates from academia, government laboratories, and industry are invited to attend the 2023 conference on "AI for Energy" hosted and co-organized by the Clean Combustion Research Center (CCRC) and the AI initiative at KAUST. We encourage you to register and attend any of the sessions on the three days. 

The conference will be organized around three topical areas:

  1. AI for fuel and engine design: This topic explores how AI can enable the design of new fuels and engines to improve the flexibility, efficiency, and cleanliness of combustion devices. Machine Learning algorithms can inform on the optimal composition of multicomponent fuel blends to achieve target performance indicators, such as autoignition time, flame speed, etc. Optimal engine geometries and operating conditions can be identified quicker using AI algorithms informed by data from measurements or CFD simulations.
  2. AI for hydrogen and renewables: This topic focuses on the potential of AI in accelerating the integration of hydrogen and renewables in the current energy landscape. AI can play a role in the design of efficient grid balancing strategies, and, for example, help inform if power from solar and/or wind should, at any given time, be used to provide electricity or be instead stored as hydrogen or ammonia for later use.
    1. AI for sensing and diagnostics in energy-related applications: This topic deals with how AI can help record, process, and act-on signals recorded in energy-related applications. AI can be used to indirectly infer quantities that are challenging or impossible to measure directly (e.g., heat release rate and species formation rates in flames) by measuring instead easily accessible quantity (e.g., flame chemiluminescence and acoustic pressure). AI can also enable the development of sensors for the early detection of undesirable events (e.g., pollutant formation and flame instabilities) in combustion chambers. In addition, there are already multiple proven applications of AI in signal and image denoising.


    Visit the conference website here for more details. 

    CCRC Newsletter

    Summer 2022 Edition

    Read here