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Research Papers

Investigation of Silica-Supported Preyssler Nanoparticles as Nanocatalysts in Alkylation of Benzene With 1-Decene Using Artificial Intelligence Approach

[+] Author and Article Information
Ali Hafizi

Department of Chemical Engineering, Faculty of Engineering,  Ferdowsi University of Mashhad, Mashhad 9177948974, Iran

Ali Ahmadpour1

Department of Chemical Engineering, Faculty of Engineering,  Ferdowsi University of Mashhad, Mashhad 9177948974, Iranahmadpour@um.ac.ir

Majid M. Heravi

Department of Chemistry,  School of Sciences, Alzahra University, Tehran 1993893973, Iran

Fatemeh F. Bamoharram

Department of Chemistry,  School of Sciences, Islamic Azad University, Mashhad branch, Mashhad 9187147578, Iran

1

Corresponding author.

J. Nanotechnol. Eng. Med 2(4), 041004 (Apr 04, 2012) (5 pages) doi:10.1115/1.4005674 History: Received May 22, 2011; Revised July 18, 2011; Published March 30, 2012; Online April 04, 2012

Silica-supported Preyssler nanoparticles were synthesized and tested in alkylation of benzene with 1-decene. Adaptive network based fuzzy inference system (ANFIS) was successfully applied for studying the operating parameters of this catalytic reaction. The reaction was carried out at a constant temperature of 80 °C for 2 h, while catalyst loading, catalyst weight percent, and benzene to 1-decene molar ratio (Bz/C10 ) were chosen as independent variables. Prediction of 1-decene conversion and linear alkylbenzene (LAB) production yield were performed by applying ANFIS method. The predictive ability and accuracy of ANFIS model were examined using unseen experimental data set and R2 was obtained to be 0.994 and 0.995 for 1-decene conversion and LAB production yield, respectively. Experimental results revealed that catalyst loading, Bz/C10 molar ratio, and catalyst weight percent have positive effect on 1-decene conversion, while increase in catalyst loading tends to decrease LAB production yield.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

TEM images of the synthesized nanostructures (the amorphous background structures are part of the TEM grid)

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Figure 2

ANFIS predicted values versus experimental results for testing data of (a) 1-decene conversion and (b) LAB production yield

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Figure 3

Surface generated for the effects of reaction parameters on 1-decene conversion (%): (a) catalyst loading (%) and catalyst weight percent (wt. %) and (b) Bz/C10 molar ratio and catalyst weight percent (wt. %)

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Figure 4

Surface generated for the effects of reaction parameters on LAB production yield (%): (a) catalyst loading (%) and catalyst weight percent (wt. %) and (b) Bz/C10 molar ratio and catalyst loading (%)

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Figure 5

ANFIS predicted and experimental values of 1-decene conversion

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Figure 6

ANFIS predicted and experimental values of LAB production yield

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