首页|South China University of Technology Reports Findings in Machine Learning (Predi ction of adsorption of metal cations by clay minerals using machine learning)
South China University of Technology Reports Findings in Machine Learning (Predi ction of adsorption of metal cations by clay minerals using machine learning)
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-New research on Machine Learning is the subject of a report. According to news originating from Guangzhou, People's Re public of China, by NewsRx correspondents, research stated, “Adsorption of heavy metals by clay minerals occurs widely at the solid-liquid interface in natural environments, and in this paper, the phenomenon of adsorption of Cd, Cu, Pb, Zn, Ni and Co by montmorillonite, kaolinite and illite was simulated using machine learning. We firstly used six machine learning models including Random Forest®, Extremely Forest(E), Gradient Boosting Decision Tree(G), Extreme Gradient Boosti ng(X), Light Gradient Boosting(LGB) and Category Boosting(CAT) to feature engine er the metal cations and the parameters of the minerals, and based on the featur e engineering results, we determined the first order hydrolysis constant(log K), solubility product constant(SPC), and higher hydrolysis constant (HHC) as the d escriptors of the metal cations, and site density(SD) and cation exchange capaci ty(CEC) as the descriptors of the clay minerals.”
GuangzhouPeople's Republic of ChinaAsiaCationsCyborgsEmerging TechnologiesInorganic ChemicalsIonsMachine LearningMinerals
NETL
NSTL
