首页|Investigators from Northwest University Release New Data on Machine Learning (Cracking of Heavy-inferior Oils With Different Alkane-aromatic Ratios To Aromatics Over Mfi Zeolites: Structureactivity Relationship Derived By Machine Learning)
Investigators from Northwest University Release New Data on Machine Learning (Cracking of Heavy-inferior Oils With Different Alkane-aromatic Ratios To Aromatics Over Mfi Zeolites: Structureactivity Relationship Derived By Machine Learning)
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NETL
NSTL
Elsevier
Investigators discuss new findings in Machine Learning. According to news reporting originating in Shaanxi, People's Republic of China, by NewsRx journalists, research stated, "This paper investigated the performance of catalysts with different morphology in cracking of heavy-inferior oil (HIO) to aromatics with different alkane-aromatic ratios (AAR), which include high and low-temperature coal tar (HMCT, SMCT), liquid products of coal-oil co-refining (LCOCR and HCOCR) and petroleum (YLP). The experimental results indicated that Na+ and OH- have a competitive effect on the catalyst morphology, and that low alkalinity in the synthesis system favors the synthesis of 2D zeolites."
ShaanxiPeople's Republic of ChinaAsiaAluminum SilicatesCyborgsEmerging TechnologiesInorganic ChemicalsMachine LearningOxidesOxygen CompoundsSilicic AcidSilicon CompoundsSilicon DioxideZeolitesNorthwest University
NETL
NSTL
Elsevier
