Title: A neural network implemented microcontroller system for quantitative classification of hazardous organic gases in the ambient air

Authors: Ali Gulbag, Fevzullah Temurtas, Cihat Tasaltin, Zafer Ziya Ozturk

Addresses: Department of Computer Engineering, Sakarya University, Esentepe Campus, Adapazari 54187, Turkey. ' Department of Computer Engineering, Sakarya University, Esentepe Campus, Adapazari 54187, Turkey. ' TUBITAK Marmara Research Center, P.O. Box 21, Gebze-Kocaeli 41470, Turkey. ' Gebze Institute of Technology, Department of Physics, Gebze-Kocaeli, Turkey

Abstract: In this study, a microcontroller-based gas mixture classification system is proposed to use real-time analyses of the trichloroethylene and acetone binary mixture. A Feed Forward Neural Network (FFNN) structure is performed for quantitative identification of individual gas concentrations (trichloroethylene and acetone) in their gas mixtures. The phthalocyaninecoated Quartz Crystal Microbalance (QCM) type sensors were used as gas sensors. A calibrated Mass Flow Controller (MFC) was used to control the flow rates of carrier gas and trichloroethylene and acetone gas mixtures streams. The components in the binary mixture were quantified by applying the sensor responses from the QCMs sensor array as inputs to the FFNN. The microcontroller-based gas mixture classification system performs Neural Network (NN)-based estimation, the data acquisition and user interface tasks. This system can estimate the gas concentrations of trichloroethylene and acetone with the average errors of 0.08% and 0.97%, respectively.

Keywords: microcontrollers; quantitative gas classification; binary mixture; QCM sensors; quartz crystal microbalance; FFNNs; feed forward neural networks; hazardous organic gases; ambient air; air pollution; air quality; trichloroethylene; acetone.

DOI: 10.1504/IJEP.2009.021823

International Journal of Environment and Pollution, 2009 Vol.36 No.1/2/3, pp.151 - 165

Available online: 05 Dec 2008 *

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