首页|Queen’s University Reports Findings in Machine Learning (Machinelearning assisted modelling of anaerobic digestion of waste activated sludge coupled with hydrothermal pre-treatment)
Queen’s University Reports Findings in Machine Learning (Machinelearning assisted modelling of anaerobic digestion of waste activated sludge coupled with hydrothermal pre-treatment)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is the subject of a report. According to newsreporting from Kingston, Canada, by NewsRx journalists, research stated, “This study utilizes decision- tree-based models, including Random Forest, XGBoost, artificial neural networks (ANNs), support vectormachine regressors, and K nearest neighbors algorithms, to predict sludge solubilization and methane yieldin hydrothermal pretreatment (HTP) coupled with anaerobic digestion (AD) processes. Analyzing twodecades of published research, we find that ANN models exhibit superior fitting accuracy for solubilizationprediction, while decision-tree models excel in methane yield prediction.”
KingstonCanadaNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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