alexa Modeling of Cu Doped Cobalt Oxide Nanocrystal Gas Sensor for Methane Detection: ANFIS Approach | OMICS International | Abstract
ISSN: 2157-7048

Journal of Chemical Engineering & Process Technology
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Research Article

Modeling of Cu Doped Cobalt Oxide Nanocrystal Gas Sensor for Methane Detection: ANFIS Approach

M. Esfandyari1*, M. Koolivand-Salooki2, Z. Sheikhi Mehrabadi1 and A. Ahmadpour1
1Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Khorasan, Iran
2Petroleum Department, National Iranian South Oil Company, Ahwaz, Iran
Corresponding Author : M. Esfandyari
Department of Chemical Engineering
Faculty of Engineering
Ferdowsi University of Mashhad
Khorasan, Iran
Tel: +98 9157082185
Fax: +98 5118816840
E-mail: [email protected]
Received October 31, 2011; Accepted February 22, 2012; Published February 27, 2012
Citation: Esfandyari M, Koolivand-Salooki M, Mehrabadi ZS, Ahmadpour A (2012)ANFIS Modeling for Synthesis and Characterization of Cu Doped Cobalt Oxide Nanocrystals for Methane Gas Sensor. J Chem Eng Process Technol 3:124. doi:10.4172/2157-7048.1000124
Copyright: © 2012 Esfandyari M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Abstract

In this paper, Nano-sized copper-cobalt compound oxide powders have been prepared by sol-gel technique with different mole ratios of Cu/Co (from 0.00 to 0.15); Detection of the methane gas, the most chemically stable hydrocarbon, is done. The structural properties and morphology of powders were studied by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and Transmission electron microscopy (TEM). XRD analysis confirms that Co3O4 and (CuO0.3CO0.7) Co2O4 phases have been formed and mean grain size were decreased with increasing dopant (from 28 to 24nm). According to TEM images it was found that the particles have cubic morphologies with nearly uniform distribution. Then an adaptive neuro-fuzzy inference system (ANFIS) models have been utilized for prediction of sensitivity values of the corresponded sensor. The results of ANFIS model show that the independent predicted Sensitivity values (S) compared to the measured target values have a good agreement. And also, high coefficient were found (R2 > 0.98) for the response.

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