首页|New Findings from School of Energy Science and Engineering in the Area of Machine Learning Described (Machine Learning-based Deoxidizer Screening for Intensifie d Hydrogen Production From Steam Splitting)
New Findings from School of Energy Science and Engineering in the Area of Machine Learning Described (Machine Learning-based Deoxidizer Screening for Intensifie d Hydrogen Production From Steam Splitting)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating from Changsha, People’s Republic of China, by NewsRx correspondents, research stated, “The design of adv anced deoxidizer is the key to promote hydrogen production from chemical looping steam splitting, however, the deoxidizer shows complicated possibility of compo sition, which results in long duration in material exploitation. In this study, Gibbs free energy change ( Delta G) is used as the output of the model, and three machine learning models, Decision Tree, Random Forest, and Gradient Boosting Tree algorithms, are established and optimized for functionalized deoxidizer scre ening.”
ChangshaPeople's Republic of ChinaAsiaCyborgsElementsEmerging TechnologiesGasesHydrogenInorganic Chemica lsMachine LearningSchool of Energy Science and Engineering
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