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Rapid determination of moisture content of multi-source solid waste using ATR-FTIR and multiple machine learning methods.
Qi, Ya-Ping; He, Pin-Jing; Lan, Dong-Ying; Xian, Hao-Yang; Lü, Fan; Zhang, Hua.
Afiliação
  • Qi YP; Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
  • He PJ; Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China; Shanghai Engineering Research Center of Multi-source Solid Wastes Co-p
  • Lan DY; Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
  • Xian HY; Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
  • Lü F; Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China; Shanghai Engineering Research Center of Multi-source Solid Wastes Co-p
  • Zhang H; Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China; Shanghai Engineering Research Center of Multi-source Solid Wastes Co-p
Waste Manag ; 153: 20-30, 2022 Nov.
Article em En | MEDLINE | ID: mdl-36041267
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Resíduos Sólidos / Poluentes Ambientais Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Resíduos Sólidos / Poluentes Ambientais Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article