首页|Reports Summarize Machine Learning Findings from Xi’an Jiaotong University (Dete rmining Supercritical Methane Adsorption Phase Density In Nanoscale Shale: From Polanyi Theory To Machine Learning)
Reports Summarize Machine Learning Findings from Xi’an Jiaotong University (Dete rmining Supercritical Methane Adsorption Phase Density In Nanoscale Shale: From Polanyi Theory To Machine Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Machine Learning are discussed in a new report. According tonews originating from Shaanxi, Peopl e’s Republic of China, by NewsRx correspondents, research stated,“The accurate and physically meaningful determination of supercritical methane adsorbed phase density(SMAPD) in shale not only aids in understanding the adsorption mechanism s but also provides crucialdesign and predictive bases for CO2 geological seque stration. This paper employs Polanyi theory, inconjunction with the properties of supercritical methane, to evaluate traditional methods for calculatingSMAPD. ”
ShaanxiPeople’s Republic of ChinaAsi aAlkanesCyborgsEmerging TechnologiesMachine LearningMethaneXi’an Jia otong University
NETL
NSTL
