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Combustion Science and Technology Volume 195, 2023 - Issue 14: 12th Mediterranean Combustion Symposium
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Research Article

Artificial Neural Networks-Based Ignition Delay Time Prediction for Natural Gas Blends

Shashank Sakleshpur NagarajaCCRC, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Makkah, Kingdom of Saudi ArabiaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-4930-6513View further author information
ORCID Icon &
S. Mani SarathyCCRC, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Makkah, Kingdom of Saudi ArabiaORCID Iconhttps://orcid.org/0000-0002-3975-6206View further author information
ORCID Icon
Pages 3248-3261 | Received 01 May 2023, Accepted 05 Jun 2023, Published online: 26 Jul 2023

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Jiayue Liu, Guijun Liu, Jiahao Wu, Guihua Zhang & Yuxin Wu. Prediction of Flame Type and Liftoff Height of Fuel Jets in Turbulent Hot Coflow Using Machine Learning Methods. Combustion Science and Technology 0:0, pages 1-23.
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Articles from other publishers (1)

Maysam Molana, Sahar Darougheh, Abbas Biglar, Ali J. Chamkha & Philip Zoldak. (2024) Machine Learning Approaches for Predicting Ignition Delay in Combustion Processes: A Comprehensive Review. Industrial & Engineering Chemistry Research 63:6, pages 2509-2518.
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