首页|New Machine Learning Study Findings Have Been Reported by Researchers at Wuhan U niversity (Ionic Liquid Binary Mixtures: Machine Learning-assisted Modeling, Sol vent Tailoring, Process Design, and Optimization)
New Machine Learning Study Findings Have Been Reported by Researchers at Wuhan U niversity (Ionic Liquid Binary Mixtures: Machine Learning-assisted Modeling, Sol vent Tailoring, Process Design, and Optimization)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news reporting out of Hubei, People's Repub lic of China, by NewsRx editors, research stated, "This work conducts a comprehe nsive modeling study on the viscosity, density, heat capacity, and surface tensi on of ionic liquid (IL)-IL binary mixtures by combining the group contribution ( GC) method with three machine learning algorithms: artificial neural network, XG Boost, and LightGBM. A large number of experimental data from reliable open sour ces is exhaustively collected to train, validate, and test the proposed ML-based GC models." Funders for this research include National Natural Science Foundation of China ( NSFC), University of Delaware, Technical University of Denmark. Our news journalists obtained a quote from the research from Wuhan University, " Furthermore, the Shapley Additive Explanations technique is employed to quantify the influential factors behind all the studied properties. Finally, these ML-ba sed GC models are sequentially integrated into computer-aided mixed solvent desi gn, process design, and optimization through an industrial case study of recover ing hydrogen from raw coke oven gas."
HubeiPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesIonic LiquidsMachine LearningSolventsWuha n University
NETL
NSTL
