Genetic algorithm modeling for photocatalytic elimination of impurity in wastewater

Raheleh  Jafari; Sina  Razvarz; Wen Yu; Alexander Gegov; Morten Goodwin

doi:10.1007/978-3-030-29516-5_17

Genetic algorithm modeling for photocatalytic elimination of impurity in wastewater

Raheleh Jafari, Sina Razvarz, Wen Yu, Alexander Gegov, Morten Goodwin

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

190 Downloads (Pure)

Abstract

The existence of C.I. Acid Yellow 23 (AY23) in water causes a great danger to people and society. Here, we suggest an advanced technique which predicts the photochemical deletion of AY23. The genetic algorithm (GA) technique is suggested in order to predict the photocatalytic removal of AY23 by implementing the Ag-TiO 2 nanoparticles provided under appropriate conditions.

In order to evaluate the proposed method, a total of 100 data are utilized which are arbitrarily divided into two: 80 samples in order to train the model as well as 20 samples in order to test the model. Experimental outcomes reveals that the suggested technique is efficient for photocatalytic elimination of impurity in wastewater.

Original language	English
Title of host publication	Intelligent Systems and Applications
Subtitle of host publication	Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 1
Editors	Yaxin Bi, Rahul Bhatia, Supriya Kapoor
Publisher	Springer
Pages	228-236
ISBN (Electronic)	978-3-030-29516-5
ISBN (Print)	978-3-030-29515-8
DOIs	https://doi.org/10.1007/978-3-030-29516-5_17
Publication status	Published - Sept 2019
Event	IEEE SAI Intelligent Systems Conference 2019 - London, United Kingdom Duration: 5 Sept 2019 → 6 Sept 2019 https://saiconference.com/IntelliSys https://saiconference.com/Conferences/IntelliSys2019

Publication series

Name	Intelligent Systems and Applications
Publisher	Springer
Volume	1037
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	IEEE SAI Intelligent Systems Conference 2019
Abbreviated title	IntelliSys 2019
Country/Territory	United Kingdom
City	London
Period	5/09/19 → 6/09/19
Internet address	https://saiconference.com/IntelliSys https://saiconference.com/Conferences/IntelliSys2019

Access to Document

10.1007/978-3-030-29516-5_17

Genetic Algorithm Modeling
The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-29516-5_17.
Accepted author manuscript (Post-print), 162 KB

Cite this

Jafari, R., Razvarz, S., Yu, W., Gegov, A., & Goodwin, M. (2019). Genetic algorithm modeling for photocatalytic elimination of impurity in wastewater. In Y. Bi, R. Bhatia, & S. Kapoor (Eds.), Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 1 (pp. 228-236). (Intelligent Systems and Applications; Vol. 1037). Springer. https://doi.org/10.1007/978-3-030-29516-5_17

Jafari, Raheleh ; Razvarz, Sina ; Yu, Wen et al. / Genetic algorithm modeling for photocatalytic elimination of impurity in wastewater. Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 1. editor / Yaxin Bi ; Rahul Bhatia ; Supriya Kapoor. Springer, 2019. pp. 228-236 (Intelligent Systems and Applications).

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title = "Genetic algorithm modeling for photocatalytic elimination of impurity in wastewater",

abstract = "The existence of C.I. Acid Yellow 23 (AY23) in water causes a great danger to people and society. Here, we suggest an advanced technique which predicts the photochemical deletion of AY23. The genetic algorithm (GA) technique is suggested in order to predict the photocatalytic removal of AY23 by implementing the Ag-TiO 2 nanoparticles provided under appropriate conditions.In order to evaluate the proposed method, a total of 100 data are utilized which are arbitrarily divided into two: 80 samples in order to train the model as well as 20 samples in order to test the model. Experimental outcomes reveals that the suggested technique is efficient for photocatalytic elimination of impurity in wastewater.",

author = "Raheleh Jafari and Sina Razvarz and Wen Yu and Alexander Gegov and Morten Goodwin",

year = "2019",

month = sep,

doi = "10.1007/978-3-030-29516-5_17",

language = "English",

isbn = "978-3-030-29515-8",

series = "Intelligent Systems and Applications",

publisher = "Springer",

pages = "228--236",

editor = "Yaxin Bi and Rahul Bhatia and Supriya Kapoor",

booktitle = "Intelligent Systems and Applications",

note = "IEEE SAI Intelligent Systems Conference 2019, IntelliSys 2019 ; Conference date: 05-09-2019 Through 06-09-2019",

url = "https://saiconference.com/IntelliSys, https://saiconference.com/Conferences/IntelliSys2019",

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Jafari, R, Razvarz, S, Yu, W, Gegov, A & Goodwin, M 2019, Genetic algorithm modeling for photocatalytic elimination of impurity in wastewater. in Y Bi, R Bhatia & S Kapoor (eds), Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 1. Intelligent Systems and Applications, vol. 1037, Springer, pp. 228-236, IEEE SAI Intelligent Systems Conference 2019, London, United Kingdom, 5/09/19. https://doi.org/10.1007/978-3-030-29516-5_17

Genetic algorithm modeling for photocatalytic elimination of impurity in wastewater. / Jafari, Raheleh ; Razvarz, Sina ; Yu, Wen et al.
Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 1. ed. / Yaxin Bi; Rahul Bhatia; Supriya Kapoor. Springer, 2019. p. 228-236 (Intelligent Systems and Applications; Vol. 1037).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Genetic algorithm modeling for photocatalytic elimination of impurity in wastewater

AU - Jafari, Raheleh

AU - Razvarz, Sina

AU - Yu, Wen

AU - Gegov, Alexander

AU - Goodwin, Morten

PY - 2019/9

Y1 - 2019/9

N2 - The existence of C.I. Acid Yellow 23 (AY23) in water causes a great danger to people and society. Here, we suggest an advanced technique which predicts the photochemical deletion of AY23. The genetic algorithm (GA) technique is suggested in order to predict the photocatalytic removal of AY23 by implementing the Ag-TiO 2 nanoparticles provided under appropriate conditions.In order to evaluate the proposed method, a total of 100 data are utilized which are arbitrarily divided into two: 80 samples in order to train the model as well as 20 samples in order to test the model. Experimental outcomes reveals that the suggested technique is efficient for photocatalytic elimination of impurity in wastewater.

AB - The existence of C.I. Acid Yellow 23 (AY23) in water causes a great danger to people and society. Here, we suggest an advanced technique which predicts the photochemical deletion of AY23. The genetic algorithm (GA) technique is suggested in order to predict the photocatalytic removal of AY23 by implementing the Ag-TiO 2 nanoparticles provided under appropriate conditions.In order to evaluate the proposed method, a total of 100 data are utilized which are arbitrarily divided into two: 80 samples in order to train the model as well as 20 samples in order to test the model. Experimental outcomes reveals that the suggested technique is efficient for photocatalytic elimination of impurity in wastewater.

U2 - 10.1007/978-3-030-29516-5_17

DO - 10.1007/978-3-030-29516-5_17

M3 - Conference contribution

SN - 978-3-030-29515-8

T3 - Intelligent Systems and Applications

SP - 228

EP - 236

BT - Intelligent Systems and Applications

A2 - Bi, Yaxin

A2 - Bhatia, Rahul

A2 - Kapoor, Supriya

PB - Springer

T2 - IEEE SAI Intelligent Systems Conference 2019

Y2 - 5 September 2019 through 6 September 2019

ER -

Jafari R, Razvarz S, Yu W, Gegov A, Goodwin M. Genetic algorithm modeling for photocatalytic elimination of impurity in wastewater. In Bi Y, Bhatia R, Kapoor S, editors, Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 1. Springer. 2019. p. 228-236. (Intelligent Systems and Applications). Epub 2019 Aug 24. doi: 10.1007/978-3-030-29516-5_17

Genetic algorithm modeling for photocatalytic elimination of impurity in wastewater

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