Artificial neural network for modeling the size of silver nanoparticles' prepared in montmorillonite/starch bionanocomposites

被引：35

作者：

Shabanzadeh, Parvaneh ^{[1
,2
]}

Yusof, Rubiyah ^{[1
,2
]}

Shameli, Kamyar ^{[2
,3
]}

机构：

[1] Univ Teknol Malaysia, Ctr Artificial Intelligence & Robot, Kuala Lumpur 54100, Malaysia

[2] Univ Teknol Malaysia, MJIIT, Johor Baharu 54100, Malaysia

[3] Univ Putra Malaysia, Fac Sci, Dept Chem, Serdang 43400, Selangor, Malaysia

来源：

JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY | 2015年 / 24卷

关键词：

Artificial neural network; Silver nanoparticles; Montmorillonite; Bionanocomposite; Modeling; RESPONSE-SURFACE METHODOLOGY; NANOCOMPOSITES; OPTIMIZATION; PREDICTION; REDUCTION; MEMBRANES; ANN;

D O I：

10.1016/j.jiec.2014.09.007

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

In this study, artificial neural network (ANN) was used to develop an approach for evaluation of silver nanoparticles (Ag-NPs) size in the bionanocomposites substrate. A multi-layer feed forward ANN was applied to correlate the output as size of Ag-NPs, with four inputs include of AgNO3 concentration, temperature of reaction, weight percentage of starch, and MMT amount. The results of proposed methodology were compared for its predictive capabilities in terms of coefficient determination (R-2) and mean square error (MSE) based on the validation data set. The model finding revealed that AgNO3 concentration content has significant effect on size of Ag-NPs. (C) 2014 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.

引用

页码：42 / 50

页数：9

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