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A modeling study by artificial neural network on process parameter optimization for silver nanoparticle production

Artificial neural network (ANN) is the most accepted method for non-parametric modelling and process optimization of chemical engineering. The paper focuses on using ANN to analyse the yield production rate of silver nanoparticles (AgNPs). The study examines the effect of AgNO3 concentration, stirr...

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Main Authors: Chowdhury, Silvia, Yusof, Faridah, Sulaiman, Nadzril, Sidek, Shahrul Na'im, Faruck, Mohammad Omer
Format: Article
Language:English
English
Published: Asian Research Publishing Network (ARPN) 2016
Subjects:
QD Chemistry
T Technology (General)
Online Access:http://irep.iium.edu.my/52645/1/jeas_1016_5222.pdf
http://irep.iium.edu.my/52645/7/52645_A%20modeling%20study%20by%20artificial%20neural%20network_SCOPUS.pdf
http://irep.iium.edu.my/52645/
http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_1016_5222.pd
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spelling my.iium.irep.526452017-03-17T08:24:56Z http://irep.iium.edu.my/52645/ A modeling study by artificial neural network on process parameter optimization for silver nanoparticle production Chowdhury, Silvia Yusof, Faridah Sulaiman, Nadzril Sidek, Shahrul Na'im Faruck, Mohammad Omer QD Chemistry T Technology (General) Artificial neural network (ANN) is the most accepted method for non-parametric modelling and process optimization of chemical engineering. The paper focuses on using ANN to analyse the yield production rate of silver nanoparticles (AgNPs). The study examines the effect of AgNO3 concentration, stirring time and tri-sodium citrate concentration on the production of AgNPs yield. The yield of AgNPs was modelled and optimized as a function of three independent variables. Furthermore, assessment of the model through the coefficient of determination (R2 = 0.9778) and mean square error (MSE) showed that the optimized production conditions were found at 1mM AgNO3 concentration,15 min of stirring time and 1% tri-sodium citrate. Optimal and maximal AgNPs production were 20.62 (Area*) of yield experimentally, which was calculated using area under the curve from UV-vis analysis in the wave length range of 350 nm to 420 nm. Meanwhile, under the same conditions, the ANN predicted value is 19.84 (Area*) of AgNPs yield with 3.95% error. Besides that, the ANN model was employed to construct an output surface plot to reveal the impact of input variable as well as figure out the interaction effect and clear representation of optimized condition. Synthesized AgNPs at optimized condition (absorbance 0.93AU at 420 nm wavelength) were then characterized using Field Emission Scanning Electron Microscopy (FESEM) and UV-vis analysis. Asian Research Publishing Network (ARPN) 2016-10 Article REM application/pdf en http://irep.iium.edu.my/52645/1/jeas_1016_5222.pdf application/pdf en http://irep.iium.edu.my/52645/7/52645_A%20modeling%20study%20by%20artificial%20neural%20network_SCOPUS.pdf Chowdhury, Silvia and Yusof, Faridah and Sulaiman, Nadzril and Sidek, Shahrul Na'im and Faruck, Mohammad Omer (2016) A modeling study by artificial neural network on process parameter optimization for silver nanoparticle production. ARPN Journal of Engineering and Applied Sciences, 11 (20). pp. 1-6. ISSN 1819-6608 http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_1016_5222.pd
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic QD Chemistry
T Technology (General)
spellingShingle QD Chemistry
T Technology (General)
Chowdhury, Silvia
Yusof, Faridah
Sulaiman, Nadzril
Sidek, Shahrul Na'im
Faruck, Mohammad Omer
A modeling study by artificial neural network on process parameter optimization for silver nanoparticle production
description Artificial neural network (ANN) is the most accepted method for non-parametric modelling and process optimization of chemical engineering. The paper focuses on using ANN to analyse the yield production rate of silver nanoparticles (AgNPs). The study examines the effect of AgNO3 concentration, stirring time and tri-sodium citrate concentration on the production of AgNPs yield. The yield of AgNPs was modelled and optimized as a function of three independent variables. Furthermore, assessment of the model through the coefficient of determination (R2 = 0.9778) and mean square error (MSE) showed that the optimized production conditions were found at 1mM AgNO3 concentration,15 min of stirring time and 1% tri-sodium citrate. Optimal and maximal AgNPs production were 20.62 (Area*) of yield experimentally, which was calculated using area under the curve from UV-vis analysis in the wave length range of 350 nm to 420 nm. Meanwhile, under the same conditions, the ANN predicted value is 19.84 (Area*) of AgNPs yield with 3.95% error. Besides that, the ANN model was employed to construct an output surface plot to reveal the impact of input variable as well as figure out the interaction effect and clear representation of optimized condition. Synthesized AgNPs at optimized condition (absorbance 0.93AU at 420 nm wavelength) were then characterized using Field Emission Scanning Electron Microscopy (FESEM) and UV-vis analysis.
format Article
author Chowdhury, Silvia
Yusof, Faridah
Sulaiman, Nadzril
Sidek, Shahrul Na'im
Faruck, Mohammad Omer
author_facet Chowdhury, Silvia
Yusof, Faridah
Sulaiman, Nadzril
Sidek, Shahrul Na'im
Faruck, Mohammad Omer
author_sort Chowdhury, Silvia
title A modeling study by artificial neural network on process parameter optimization for silver nanoparticle production
title_short A modeling study by artificial neural network on process parameter optimization for silver nanoparticle production
title_full A modeling study by artificial neural network on process parameter optimization for silver nanoparticle production
title_fullStr A modeling study by artificial neural network on process parameter optimization for silver nanoparticle production
title_full_unstemmed A modeling study by artificial neural network on process parameter optimization for silver nanoparticle production
title_sort modeling study by artificial neural network on process parameter optimization for silver nanoparticle production
publisher Asian Research Publishing Network (ARPN)
publishDate 2016
url http://irep.iium.edu.my/52645/1/jeas_1016_5222.pdf
http://irep.iium.edu.my/52645/7/52645_A%20modeling%20study%20by%20artificial%20neural%20network_SCOPUS.pdf
http://irep.iium.edu.my/52645/
http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_1016_5222.pd
_version_ 1643614201573277696
score 13.211869

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