Intelligent modeling of metal oxide gas sensor

Joint Authors

Lutfi, Umar Faruq
Abd al-Hamid, Ala Ala al-Din

Source

Engineering and Technology Journal

Issue

Vol. 36, Issue 7A (31 Jul. 2018), pp.777-783, 7 p.

Publisher

University of Technology

Publication Date

2018-07-31

Country of Publication

Iraq

No. of Pages

7

Main Subjects

Engineering Sciences and Information Technology

Abstract EN

Due to the complexity of the gas detection process, traditional modeling techniques cannot provide accurate modeling performance to reproduce the behavior of this difficult process.

In this paper, an intelligent modeling technique is utilized to develop an accurate model to represent the complex and nonlinear gas detection process.

In particular, in this study nickel Oxide NiO gas sensor, which was specifically fabricated by a simple chemical spray pyrolysis technique.

In the process, the nickel chloride hexahydrate salt was used at a concentration of (0.05 M) and a temperature of 350 ºC.

Because of this process, the thickness of NiO was 0.1μm.

Inspection was done using three different testing techniques; X-ray diffraction, scanning electron microscopy, and the sensitivity test of NiO for Methane gas CH4 in the range of (0-500) ppmv.

Inspection results show that the film was crystalline, has a cubic system, and without cracks or open pores.

On the other hand, the sensitivity results were disparate and low in value within the considered range.

From the real-time experiment described above, training samples were gathered to develop the desired process model.

The considered modeling technique was based on exploiting the wavelet network (wavenet) to represent the nonlinear function of the nonlinear autoregressive with exogenous input (NARX) structure.

In model development process, the experimental data were utilized as the training samples for the wavenet-based NARX model.

As the modeling accuracy, the proposed wavenet-based NARX model attained a value of 1.895 × 10-12 for the root mean square of error (RMSE) criterion.

American Psychological Association (APA)

Lutfi, Umar Faruq& Abd al-Hamid, Ala Ala al-Din. 2018. Intelligent modeling of metal oxide gas sensor. Engineering and Technology Journal،Vol. 36, no. 7A, pp.777-783.
https://search.emarefa.net/detail/BIM-834401

Modern Language Association (MLA)

Lutfi, Umar Faruq& Abd al-Hamid, Ala Ala al-Din. Intelligent modeling of metal oxide gas sensor. Engineering and Technology Journal Vol. 36, no. 7A (2018), pp.777-783.
https://search.emarefa.net/detail/BIM-834401

American Medical Association (AMA)

Lutfi, Umar Faruq& Abd al-Hamid, Ala Ala al-Din. Intelligent modeling of metal oxide gas sensor. Engineering and Technology Journal. 2018. Vol. 36, no. 7A, pp.777-783.
https://search.emarefa.net/detail/BIM-834401

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references : p. 782-783

Record ID

BIM-834401