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

作     者：Gulbag, Ali Temurtas, Feyzullah Tasaltin, Cihat Ozturk, Zafer Ziya 

作者机构：Sakarya Univ Dept Comp Engn TR-54187 Adapazari Turkey TUBITAK Marmara Res Ctr TR-41470 Gebze Turkey Gebze Inst Technol Dept Phys TR-41400 Gebze Turkey 

出 版 物：《INTERNATIONAL JOURNAL OF ENVIRONMENT AND POLLUTION》 (国际环境与污染杂志)

年 卷 期：2009年第36卷第1-3期

页      面：151-165页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 08[工学] 

主　　题：NN neural network microcontroller implementation quantitative gas classification binary mixture QCM quartz crystal microbalance sensors FFNNs feed forward neural networks 

摘      要：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 phthalocyanine-coated 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.