期刊
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
卷 23, 期 3, 页码 729-740出版社
Tubitak Scientific & Technological Research Council Turkey
DOI: 10.3906/elk-1304-210
关键词
Electronic nose; E-Nose; air quality monitoring; artificial neural networks
资金
- Dumlupmar University [2010-10]
A system for monitoring and predicting indoor air quality level is proposed in this paper. The system comprises a computer with a monitoring program and a sensor cell, which has an array of metal oxide gas sensors along with a temperature and humidity sensor. The gas sensors in the cell have been chosen to detect only hydrogen, methane, and carbon monoxide gases. Methane was selected as a representative for indoor combustible gases, and carbon monoxide was used to represent indoor toxic gases. Hydrogen was used as an interfering (and also combustible) gas in the study. A number of experiments were conducted to train the three artificial neural networks of the monitoring system. The networks have been trained using 80% of the gathered data with the Levenberg-Marquardt algorithm. The results of this work show that the performance rate of the proposed monitoring system in determining gas type for the limited sample space is 100% even when there is an interfering gas such as hydrogen in the environment. The trained system can predict the concentration level of the methane and carbon dioxide gases with a low absolute mean percent error rate of almost 1%.
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