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%.

Anahtar Kelimeler:

Electronic nose, E-Nose, air quality monitoring, artificial neural networks

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

Keywords:

Electronic nose, E-Nose, air quality monitoring, artificial neural networks,

PDF

___

es in Pamplona, Spain. Atmos Environ 2008; 42: 6647–6654.
Ashmore MR, Dimitroulopoulou C. Personal exposure of children to air pollution. Atmos Environ 2009; 43: 128–141.
Yu TC, Lin CC, Chen CC, Lee WL, Lee RG, Tseng CH, Liu SP. Wireless sensor networks for indoor air quality monitoring. Med Eng Phys 2013; 35: 231–235.
Kim M, Sankara Rao B, Kang O, Kim J, Yoo C. Monitoring and prediction of indoor air quality (IAQ) in subway or metro systems using season dependent models. Energ Buildings 2012; 46: 48–55.
Gardner JW, Bartlett PN. A brief history of electronic nose . Sensor Actuat B-Chem 1994; 18: 210–211.
Ampuero S, Bosset JO. The electronic nose applied to dairy products: a review. Sensor Actuat B-Chem 2003; 94: 1–
Kateb B, Ryan MA, Homer ML, Lara LM, Yin Y, Higa K, Chen MY. Sniffing out cancer using the JPL electronic nose: a pilot study of a novel approach to detection and differentiation of brain cancer. NeuroImage 2009; 47: T5–T
Hakim B, Zohir D. Enhancement of the neural network modeling accuracy using a submodeling decomposition-based technique, application in gas sensor. Neural Comput Appl 2012; 21: 1981–1986.
Patterson DW. Introduction to Artificial Intelligence and Expert Systems. Englewood Cliffs, NJ, USA: Prentice Hall, 1990.
Hagan MT, Demuth HB, Beale MH. Neural Network Design. Boston, MA, USA: PWS Publishing, 1996.
Haykin SS. Neural Networks: A Comprehensive Foundation. Englewood Cliffs, NJ, USA: Prentice Hall, 1999.
Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE T Neural Network 1994; 5: 989–993.
Mumyakmaz B, ¨Ozmen A, Ebeo˘glu MA, Ta¸saltın C. Predicting gas concentrations of ternary gas mixtures for a predefined 3D sample space. Sensor Actuat B-Chem 2008; 128: 594–602.
Mumyakmaz B, ¨Ozmen A, Ebeo˘glu MA, Ta¸saltın C, G¨urol ˙I. A study on the development of a compensation method for humidity effect in QCM sensor responses. Sensor Actuat B-Chem 2010; 147: 277–282.