A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

被引：35

作者：

Fiyadh, Seef Saadi ^{[1
]}

Alardhi, Saja Mohsen ^{[2
]}

Al Omar, Mohamed ^{[3
]}

Aljumaily, Mustafa M. ^{[3
]}

Al Saadi, Mohammed Abdulhakim ^{[3
]}

Fayaed, Sabah Saadi ^{[3
,4
]}

Ahmed, Sulaiman Nayef ^{[5
]}

Salman, Ali Dawood ^{[6
,7
]}

Abdalsalm, Alyaa H. ^{[2
]}

Jabbar, Noor Mohsen ^{[8
]}

El-Shafi, Ahmed ^{[9
]}

机构：

[1] Cent Stat Org, Minist Planning, Anbar, Iraq

[2] Univ Technol Baghdad, Nanotechnol & Adv Mat Res Ctr, Baghdad, Iraq

[3] Al Maarif Univ Coll, Dept Civil Engn, Ramadi, Iraq

[4] Minist Planning Dept, Social Serv Projects Sect, Baghdad, Iraq

[5] Univ Fallujah, Construct & Projects Dept, Fallujah, Iraq

[6] Univ Pannonia, Sustainabil Solut Res Lab, Egyet Str 10, H-8200 Veszprem, Hungary

[7] Basra Univ Oil & Gas, Coll Oil & Gas Engn, Dept Chem & Petr Refining Engn, Basra, Iraq

[8] Univ Baghdad, Al Khwarizmi Coll Engn, Biochem Engn Dept, Baghdad, Iraq

[9] Univ Malaya, Dept Civil Engn, Kuala Lumpur 50603, Malaysia

来源：

HELIYON | 2023年 / 9卷 / 04期

关键词：

Adsorption process; Artificial neural network; Water treatment; Environmental modelling; Heavy metals; RESPONSE-SURFACE METHODOLOGY; SOLAR-RADIATION PREDICTION; AQUEOUS-SOLUTION; COMPRESSIVE STRENGTH; PB(II) ADSORPTION; ARSENIC REMOVAL; LEAD REMOVAL; ANN APPROACH; OPTIMIZATION; BIOSORPTION;

D O I：

10.1016/j.heliyon.2023.e15455

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：11

共 117 条

[1] Productivity modelling of a developed inclined stepped solar still system based on actual performance and using a cascaded forward neural network model [J].