首页|Studies from University of Science and Technology Beijing Update Current Data on Machine Learning (Prediction of Desulfurization Efficiency and Costs During Kan bara Reactor Hot Metal Treatment Using Machine Learning)
Studies from University of Science and Technology Beijing Update Current Data on Machine Learning (Prediction of Desulfurization Efficiency and Costs During Kan bara Reactor Hot Metal Treatment Using Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting originating in Beijing, Peo ple's Republic of China, by NewsRx journalists, research stated, "A machine lear ning model was developed to predict the desulfurization process during the Kanba ra reactor hot metal treatment. Compared with other algorithms, the LR algorithm model exhibited the smallest error in current calculations, which was used to p redict the final S content with various operation parameters." The news reporters obtained a quote from the research from the University of Sci ence and Technology Beijing, "The final S content in the hot metal obviously ros e from 0.001% to higher than 0.003% with the increas e of the initial S content from 0.03% to 0.06%, while it decreased from 0.003% to below 0.001 % with the i ncrease from desulfurizer addition from 4 kg/ton to 7 kg/ton. The final S conten t changed little with the increase of C content, Mn content, and rotation speed. The feature selection using RReliefF algorithm was conducted to evaluate the co rrelation between inputted parameters and outputted final S content. The additio n of desulfurizers was beneficial to improve the desulfurization efficiency, whi le it obviously increased desulfurization costs."
BeijingPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningUniversity of Science and T echnology Beijing
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