首页|New Machine Learning Findings from Beijing Technology and Business University Discussed (Tsc Prediction and Dynamic Control of Bof Steelmaking With State-of-the-art Machine Learning and Deep Learning Methods)
New Machine Learning Findings from Beijing Technology and Business University Discussed (Tsc Prediction and Dynamic Control of Bof Steelmaking With State-of-the-art Machine Learning and Deep Learning Methods)
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NETL
NSTL
Springer Nature
Researchers detail new data in Machine Learning. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Mathematical (data-driven) models based on state-of-the-art (SOTA) machine learning and deep learning models and data collected from 12,786 heats were established to predict the values of temperature, sample, and carbon (TSC) test, including temperature of molten steel (TSC-Temp), carbon content (TSC-C) and phosphorus content (TSC-P), which made preparation for eliminating the TSC test. To maximize the prediction accuracy of the proposed approach, various models with different inputs were implemented and compared, and the best models were applied to the production process of a Hesteel Group steelmaking plant in China in the field.”
BeijingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningBeijing Technology and Business University
NETL
NSTL
Springer Nature
