首页|Study Findings on Machine Learning Discussed by a Researcher at University of Sa skatchewan (Prediction of Individual Gas Yields of Supercritical Water Gasificat ion of Lignocellulosic Biomass by Machine Learning Models)
Study Findings on Machine Learning Discussed by a Researcher at University of Sa skatchewan (Prediction of Individual Gas Yields of Supercritical Water Gasificat ion of Lignocellulosic Biomass by Machine Learning Models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news originating from Saskatoon, Canada, by News Rx editors, the research stated, “Supercritical water gasification (SCWG) of lig nocellulosic biomass is a promising pathway for the production of hydrogen.” Funders for this research include Natural Sciences And Engineering Research Coun cil of Canada. Our news journalists obtained a quote from the research from University of Saska tchewan: “However, SCWG is a complex thermochemical process, the modeling of whi ch is challenging via conventional methodologies. Therefore, eight machine learn ing models (linear regression (LR), Gaussian process regression (GPR), artificia l neural network (ANN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and categorical boosting regresso r (CatBoost)) with particle swarm optimization (PSO) and a genetic algorithm (GA ) optimizer were developed and evaluated for prediction of H2, CO, CO2, and CH4 gas yields from SCWG of lignocellulosic biomass. A total of 12 input features of SCWG process conditions (temperature, time, concentration, pressure) and biomas s properties (C, H, N, S, VM, moisture, ash, real feed) were utilized for the pr ediction of gas yields using 166 data points. Among machine learning models, boo sting ensemble tree models such as XGB and CatBoost demonstrated the highest pow er for the prediction of gas yields. PSO-optimized XGB was the best performing m odel for H2 yield with a test R2 of 0.84 and PSO-optimized CatBoost was best for prediction of yields of CH4, CO, and CO2, with test R2 values of 0.83, 0.94, an d 0.92, respectively. The effectiveness of the PSO optimizer in improving the pr ediction ability of the unoptimized machine learning model was higher compared t o the GA optimizer for all gas yields.”
University of SaskatchewanSaskatoonC anadaNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learni ng
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