An E-Nose-based indoor air quality monitoring system: prediction of combustible and toxic gas concentrations

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%.