首页|Chinese Academy of Sciences Reports Findings in Machine Learning (Thermodynamics and explainable machine learning assist in interpreting biodegradability of dis solved organic matter in sludge anaerobic digestion with thermal hydrolysis)
Chinese Academy of Sciences Reports Findings in Machine Learning (Thermodynamics and explainable machine learning assist in interpreting biodegradability of dis solved organic matter in sludge anaerobic digestion with thermal hydrolysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news originating from Beijing, People’s Republic of Chi na, by NewsRx correspondents, research stated, “Dissolved organic matter (DOM) i s essential in biological treatment, yet its specific roles remain incompletely understood. This study introduces a machine learning (ML) framework to interpret DOM biodegradability in the anaerobic digestion (AD) of sludge, incorporating a thermodynamic indicator (l).” Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, “Ensemble models such as Xgboost and LightGBM achieved high accura cy (training: 0.90-0.98; testing: 0.75-0.85). The explainability of the ML model s revealed that the features l, measured m/z, nitrogen to carbon ratio (N/C), hy drogen to carbon ratio (H/C), and nominal oxidation state of carbon (NOSC) were significant formula features determining biodegradability. Shapley values furthe r indicated that the biodegradable DOM were mostly formulas with l lower than 0. 03, measured m/z value higher than 600 Da, and N/C ratios higher than 0.2.”
BeijingPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningPhysicsThermodynamics
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