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A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique.
Fiyadh, Seef Saadi; Alardhi, Saja Mohsen; Al Omar, Mohamed; Aljumaily, Mustafa M; Al Saadi, Mohammed Abdulhakim; Fayaed, Sabah Saadi; Ahmed, Sulaiman Nayef; Salman, Ali Dawood; Abdalsalm, Alyaa H; Jabbar, Noor Mohsen; El-Shafi, Ahmed.
Afiliação
  • Fiyadh SS; Ministry of Planning, Central Statistical Organization, Anbar, Iraq.
  • Alardhi SM; Nanotechnology and Advanced Materials Research Center, University of Technology, Iraq.
  • Al Omar M; Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq.
  • Aljumaily MM; Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq.
  • Al Saadi MA; Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq.
  • Fayaed SS; Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq.
  • Ahmed SN; Ministry of Planning Dept. Social Services Projects Section, Baghdad, Iraq.
  • Salman AD; Construction and Projects Department, University of Fallujah, Iraq.
  • Abdalsalm AH; Sustainability Solutions Research Lab, University of Pannonia, Egyetem Str. 10, H-8200 Veszprem, Hungary.
  • Jabbar NM; Department of Chemical and Petroleum Refining Engineering, College of Oil and Gas Engineering, Basra University for Oil and Gas, Iraq.
  • El-Shafi A; Nanotechnology and Advanced Materials Research Center, University of Technology, Iraq.
Heliyon ; 9(4): e15455, 2023 Apr.
Article em En | MEDLINE | ID: mdl-37128319
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article