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Predicting bilgewater emulsion stability by oil separation using image processing and machine learning.
Lee, Woo Hyoung; Park, Cheol Young; Diaz, Daniela; Rodriguez, Kelsey L; Chung, Jongik; Church, Jared; Willner, Marjorie R; Lundin, Jeffrey G; Paynter, Danielle M.
Afiliação
  • Lee WH; Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, United States. Electronic address: woohyoung.lee@ucf.edu.
  • Park CY; Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, United States.
  • Diaz D; Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, United States.
  • Rodriguez KL; Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, United States.
  • Chung J; Department of Statistics and Data Science, University of Central Florida, Orlando, FL 32816-2370, United States.
  • Church J; Environmental Engineering, Science, and Technology Branch, Naval Surface Warfare Center, Carderock Division, West Bethesda, MD, United States.
  • Willner MR; Environmental Engineering, Science, and Technology Branch, Naval Surface Warfare Center, Carderock Division, West Bethesda, MD, United States.
  • Lundin JG; Chemistry Division, United States Naval Research Laboratory, Washington, D.C., United States.
  • Paynter DM; Environmental Engineering, Science, and Technology Branch, Naval Surface Warfare Center, Carderock Division, West Bethesda, MD, United States.
Water Res ; 223: 118977, 2022 Sep 01.
Article em En | MEDLINE | ID: mdl-35988334
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Óleos / Águas Residuárias Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Water Res Ano de publicação: 2022 Tipo de documento: Article País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Óleos / Águas Residuárias Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Water Res Ano de publicação: 2022 Tipo de documento: Article País de publicação: Reino Unido