Intelligent Modeling of Metal Oxide Gas Sensor | ||
| Engineering and Technology Journal | ||
| Article 10, Volume 36, 7A, July 2018, Pages 777-783 PDF (986.52 K) | ||
| DOI: 10.30684/etj.36.7A.10 | ||
| Authors | ||
| Omar F. Lutfy* 1; Alaa A. Abdul-Hamead2 | ||
| 1Control and Systems Engineering Department, University of Technology-Iraq - Iraq | ||
| 2Materials Engineering Department University of Technology, Baghdad, Iraq | ||
| Abstract | ||
| 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. | ||
| Keywords | ||
| Gas Sensor; metal oxides; nonlinear autoregressive with exogenous input (NARX); pollutant gases; Spray Pyrolysis; wavelet network | ||
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