首页|Findings from University of Waterloo Reveals New Findings on Machine Learning (D evelopment of Explicit Models To Predict Methane Hydrate Equilibrium Conditions In Pure Water and Brine Solutions: a Machine Learning Approach)
Findings from University of Waterloo Reveals New Findings on Machine Learning (D evelopment of Explicit Models To Predict Methane Hydrate Equilibrium Conditions In Pure Water and Brine Solutions: a Machine Learning Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting originating from Waterloo, Canada, b y NewsRx correspondents, research stated, “An important phase in the design of p rocesses involving gas hydrates is predicting the hydrate formation conditions. In this study, three explicit correlations based on machine learning techniques, gene expression programming (GEP), and optimizing two correlations with predete rmined structures were developed for the prediction of methane hydrate formation temperature (HFT) in pure water and brines.” Financial support for this research came from Natural Sciences and Engineering Research Council of Canada (NSERC).
WaterlooCanadaNorth and Central AmericaAlkanesCyborgsEmerging TechnologiesMachine LearningMethaneUnivers ity of Waterloo
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