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

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/
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
_version_ 1848784094327996416
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
building IIUM Repository
collection Online Access
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.
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institution International Islamic University Malaysia
institution_category Local University
language English
English
last_indexed 2025-11-14T16:31:47Z
publishDate 2016
publisher Asian Research Publishing Network (ARPN)
recordtype eprints
repository_type Digital Repository
spelling iium-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 PeerReviewed 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
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
title 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_short 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
topic QD Chemistry
T Technology (General)
url http://irep.iium.edu.my/52645/
http://irep.iium.edu.my/52645/
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

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