首页|Research from University of Alberta Provides New Data on Machine Learning (Trans ient NOx emission modeling of a hydrogen-diesel engine using hybrid machine lear ning methods)
Research from University of Alberta Provides New Data on Machine Learning (Trans ient NOx emission modeling of a hydrogen-diesel engine using hybrid machine lear ning methods)
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Investigators publish new report on ar tificial intelligence. According to news reporting out of Edmonton, Canada, by N ewsRx editors, research stated, "One promising approach to reduce carbon foot pr int of internal combustion engines (ICEs) is using alternative fuels like hydrog en, particularly by converting medium and heavy-duty diesel engines to dual-fuel hydrogen-diesel engines. To minimize elevated NOx emissions from hydrogen-fuele d engine, fast and accurate emission models are essential for engine model-based control and for engine calibration and optimization using hardware-in-the-loop (HIL) setups."
University of AlbertaEdmontonCanadaNorth and Central AmericaCyborgsElementsEmerging TechnologiesGasesHyd rogenInorganic ChemicalsMachine Learning
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