首页|Study Findings from Tianjin University Provide New Insights into Machine Learning (Based On Machine Learning Model for Prediction of Co2 Adsorption of Synthetic Zeolite In Two-step Solid Waste Treatment)
Study Findings from Tianjin University Provide New Insights into Machine Learning (Based On Machine Learning Model for Prediction of Co2 Adsorption of Synthetic Zeolite In Two-step Solid Waste Treatment)
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NETL
NSTL
Elsevier
Investigators publish new report on Machine Learning. According to news reporting out of Tianjin, People's Republic of China, by NewsRx editors, research stated, “The rising environmental issues caused by carbon dioxide emissions and accumulation of industrial solid waste accelerate the development of carbon capture utilization and storage (CCUS), especially the technology using industrial solid waste as a raw material to prepare environmentally friendly and sustainable porous materials to capture CO2. This study developed four models including support vector regression(SVR), multivariate adaptive regression spline(Mars), random forest(RF), and gradient boosting machine(GBM) based on 762 CO2 adsorption datasets of zeolites synthesized from five different industrial solid waste materials to predict the CO2 adsorption capacity and analyze impact of various factors on CO2 adsorption performance during synthesis and adsorption processes.”
TianjinPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningSupport Vector RegressionTianjin University
NETL
NSTL
Elsevier
