首页|Findings on Machine Learning Discussed by Investigators at Xi'an Jiaotong Univer sity (Exploring Novel Heat Transfer Correlations: Machine Learning Insights for Molten Salt Heat Exchangers)
Findings on Machine Learning Discussed by Investigators at Xi'an Jiaotong Univer sity (Exploring Novel Heat Transfer Correlations: Machine Learning Insights for Molten Salt Heat Exchangers)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting out of Xi'an, People's Republic of China, by NewsRx editors, research stated, "The utilization of molten salts in heat tr ansfer applications, specifically within shell-and-tube heat exchangers, has gar nered significant attention for its potential in sustainable energy solutions. t his study employs advanced machine learning algorithms, including decision tree regressor, support vector regressor, extreme gradient boosting, and random fores t, to not only predict the heat transfer behavior of molten salts but also unrav el the complex mechanisms underlying this process. Achieving a remarkable accura cy score of 0.985, the Support Vector Regressor leads the predictive models, clo sely followed by random forest (0.982), Decision Tree Regressor (0.974), and Ext reme Gradient Boosting (0.965)." Our news journalists obtained a quote from the research from Xi'an Jiaotong Univ ersity, "The incorporation of Shapley Additive exPlanations values accentuates t he Reynolds number's pivotal role, elucidating a robust correlation with the Nus selt value. These insights transcend mere prediction, offering a profound unders tanding that can significantly impact the design and optimization of molten salt heat exchangers." According to the news editors, the research concluded: "The applications of molt en salts extend across various sectors, including concentrated solar energy and thermal storage, solidifying their position as a versatile and effective solutio n in the pursuit of sustainable and efficient energy systems."
Xi'anPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningXi'an Jiaotong University