Literature DB >> 28473053

Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: A review.

Abdol Mohammad Ghaedi1, Azam Vafaei2.   

Abstract

Artificial neural networks (ANNs) have been widely applied for the prediction of dye adsorption during the last decade. In this paper, the applications of ANN methods, namely multilayer feedforward neural networks (MLFNN), support vector machine (SVM), and adaptive neuro fuzzy inference system (ANFIS) for adsorption of dyes are reviewed. The reported researches on adsorption of dyes are classified into four major categories, such as (i) MLFNN, (ii) ANFIS, (iii) SVM and (iv) hybrid with genetic algorithm (GA) and particle swarm optimization (PSO). Most of these papers are discussed. The further research needs in this field are suggested. These ANNs models are obtaining popularity as approaches, which can be successfully employed for the adsorption of dyes with acceptable accuracy.
Copyright © 2017. Published by Elsevier B.V.

Keywords:  ANFIS; ANN; Adsorption; Dye; Removal; SVM

Year:  2017        PMID: 28473053     DOI: 10.1016/j.cis.2017.04.015

Source DB:  PubMed          Journal:  Adv Colloid Interface Sci        ISSN: 0001-8686            Impact factor:   12.984


  12 in total

1.  Occurrence and risk factors of brucellosis among domestic animals: an artificial neural network approach.

Authors:  Majid ZareBidaki; Elaheh Allahyari; Tayebeh Zeinali; Mohamad Asgharzadeh
Journal:  Trop Anim Health Prod       Date:  2022-01-17       Impact factor: 1.559

2.  Thiourea-Isocyanate-Based Covalent Organic Frameworks with Tunable Surface Charge and Surface Area for Methylene Blue and Methyl Orange Removal from Aqueous Media.

Authors:  Selin S Suner; Sahin Demirci; Duygu S Sutekin; Selehattin Yilmaz; Nurettin Sahiner
Journal:  Micromachines (Basel)       Date:  2022-06-13       Impact factor: 3.523

3.  Applying artificial neural-network model to predict psychiatric symptoms.

Authors:  Elahe Allahyari; Narges Roustaei
Journal:  Biomedicine (Taipei)       Date:  2022-03-01

4.  Appraisal of Cu(ii) adsorption by graphene oxide and its modelling via artificial neural network.

Authors:  Yumeng Zhang; Min Dai; Ke Liu; Changsheng Peng; Yufeng Du; Quanchao Chang; Imran Ali; Iffat Naz; Devendra P Saroj
Journal:  RSC Adv       Date:  2019-09-24       Impact factor: 4.036

5.  Experimental and predictive study on the performance and energy consumption characteristics for the regeneration of activated alumina assisted by ultrasound.

Authors:  Xinzhu Mou; Zhenqian Chen
Journal:  Ultrason Sonochem       Date:  2020-08-24       Impact factor: 7.491

6.  BP-ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System.

Authors:  Yu Mei; Jiaqian Yang; Yin Lu; Feilin Hao; Dongmei Xu; Hua Pan; Jiade Wang
Journal:  Int J Environ Res Public Health       Date:  2019-07-10       Impact factor: 3.390

7.  An artificial neural network combined with response surface methodology approach for modelling and optimization of the electro-coagulation for cationic dye.

Authors:  Manisha S Kothari; Kinjal G Vegad; Kosha A Shah; Ashraf Aly Hassan
Journal:  Heliyon       Date:  2022-01-12

8.  Predicting mental health of prisoners by artificial neural network.

Authors:  Elahe Allahyari; Mozhgan Moshtagh
Journal:  Biomedicine (Taipei)       Date:  2021-03-01

9.  Adsorption of dicamba and MCPA onto MIL-53(Al) metal-organic framework: response surface methodology and artificial neural network model studies.

Authors:  Hamza Ahmad Isiyaka; Khairulazhar Jumbri; Nonni Soraya Sambudi; Zakariyya Uba Zango; Nor Ain Fathihah Abdullah; Bahruddin Saad; Adamu Mustapha
Journal:  RSC Adv       Date:  2020-11-27       Impact factor: 4.036

10.  Bubbly flow prediction with randomized neural cells artificial learning and fuzzy systems based on k-ε turbulence and Eulerian model data set.

Authors:  Meisam Babanezhad; Mahboubeh Pishnamazi; Azam Marjani; Saeed Shirazian
Journal:  Sci Rep       Date:  2020-08-14       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.