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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 SS 1 , Alardhi SM 2 , Al Omar M 3 , Aljumaily MM 3 , Al Saadi MA 3 , Fayaed SS 3 Show all authors , Ahmed SN 4 , Salman AD 5 , Abdalsalm AH 2 , Jabbar NM 6 , El-Shafi A 7

Affiliations 

  • 1 Ministry of Planning, Central Statistical Organization, Anbar, Iraq
  • 2 Nanotechnology and Advanced Materials Research Center, University of Technology, Iraq
  • 3 Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq
  • 4 Construction and Projects Department, University of Fallujah, Iraq
  • 5 Sustainability Solutions Research Lab, University of Pannonia, Egyetem Str. 10, H-8200 Veszprem, Hungary
  • 6 Biochemical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
  • 7 Department of Civil Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia
Heliyon, 2023 Apr;9(4):e15455.
PMID: 37128319 DOI: 10.1016/j.heliyon.2023.e15455

Abstract

Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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