首页|Reports on Machine Learning from Southeast University Provide New Insights (Digi tal Twins Based On Machine Learning for Optimal Control of Chemical Looping Hydr ogen Generation Processes)
Reports on Machine Learning from Southeast University Provide New Insights (Digi tal Twins Based On Machine Learning for Optimal Control of Chemical Looping Hydr ogen Generation Processes)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating from Nanjing, P eople’s Republic of China, by NewsRx correspondents, research stated, “For a che mical looping hydrogen generation system, the set of input gas flow rate needed to be adjusted on the reactivity of oxygen carriers, in order to guarantee gas c onversion and to obtain higher hydrogen purity. However, the oxygen carriers oft en decayed upon repeated cycles, leading to pressing need for dynamic alignment between oxygen carrier reactivity and process parameters.”
NanjingPeople’s Republic of ChinaAsi aChalcogensCyborgsElementsEmerging TechnologiesGasesHydrogenInorga nic ChemicalsMachine LearningSoutheast University
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