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Interpretation and Prediction of the CO2 Sequestration of Steel Slag by Machine Learning.
He, Bingyang; Zhu, Xingyu; Cang, Zhizhi; Liu, Yang; Lei, Yuxin; Chen, Zhaohou; Wang, Yanlin; Zheng, Yongchao; Cang, Daqiang; Zhang, Lingling.
Afiliação
  • He B; School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
  • Zhu X; Department of Electronic and Information Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China.
  • Cang Z; Beijing Building Materials Academy of Sciences Research, Beijing 100041, PR China.
  • Liu Y; School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
  • Lei Y; School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
  • Chen Z; School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
  • Wang Y; School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
  • Zheng Y; Beijing Building Materials Academy of Sciences Research, Beijing 100041, PR China.
  • Cang D; School of Metallurgy and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China.
  • Zhang L; School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Environ Sci Technol ; 57(46): 17940-17949, 2023 Nov 21.
Article em En | MEDLINE | ID: mdl-37624988
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Dióxido de Carbono / Resíduos Industriais Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Environ Sci Technol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Dióxido de Carbono / Resíduos Industriais Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Environ Sci Technol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China