Mathematical modeling for SnO2 gas sensor based on second-order response

Sensors, work on the entrance gate for exploring dynamic environments and system response. The sensor behavior plays an important role in system modeling. Therefore, finding the mathematical model, which can describe the sensor response with maximum information over time in different environments and situations, is necessary. This paper, attempts to model the optimal transfer function of the SnO2 type gas sensor (TGS 813) for transient response and steady-state zones, based on the acquired data of the sensor. The model could be used to obtain the mathematical equation for the response, as a transfer function based on different concentrations, for the specific temperature and humidly. The data analysis has been done with Matlab software, and Genetic algorithm is further used to optimize the transfer function parameters. Moreover, the effect of variation in concentration on the transfer function coefficients has been explored. The results show that the modeling can be helpful for sensor response behavior simulation as well as physical and chemical reaction investigation.

[1]  Alcherio Martinoli,et al.  A comparison of casting and spiraling algorithms for odor source localization in laminar flow , 2008, 2008 IEEE International Conference on Robotics and Automation.

[2]  T. Moriizumi,et al.  Controlling a gas/odor plume-tracking robot based on transient responses of gas sensors , 2002, Proceedings of IEEE Sensors.

[3]  C. Distante,et al.  On the study of feature extraction methods for an electronic nose , 2002 .

[4]  Doron Lancet,et al.  A feature extraction method for chemical sensors in electronic noses , 2003 .

[5]  Ata Jahangir Moshayedi,et al.  Priority based algorithm for Plume tracking Robot , 2012, 2012 1st International Symposium on Physics and Technology of Sensors (ISPTS-1).

[6]  Lino Marques,et al.  Olfaction-based mobile robot navigation , 2002 .

[7]  Silvia Coradeschi,et al.  Relying on an electronic nose for odor localization , 2002, 2002 IEEE International Symposium on Virtual and Intelligent Measurement Systems (IEEE Cat. No.02EX545).

[8]  Marta Padilla Ferrán.  Improving the robustness of artificial olfaction systems by multivariate signal processing , 2010 .

[9]  Gotan H. Jain,et al.  MOS gas sensors: What determines our choice? , 2011, 2011 Fifth International Conference on Sensing Technology.

[10]  Shannon E. Stitzel,et al.  Cross-reactive chemical sensor arrays. , 2000, Chemical reviews.

[11]  Katsuhiko Ogata,et al.  Modern Control Engineering , 1970 .

[12]  Nathan S. Lewis,et al.  Cross-Reactive Chemical Sensor Arrays , 2000 .

[13]  H. T. Nagle,et al.  Transient response analysis of an electronic nose using multi-exponential models , 1999 .

[14]  Jürgen Wöllenstein,et al.  Comparison of gas sensor technologies for fire gas detection , 2011 .

[15]  Pierre Grenier,et al.  Optimisation of electronic nose measurements. Part II : Influence of experimental parameters , 1999 .

[16]  Toshinobu Harada,et al.  Data fitting with a spline using a real-coded genetic algorithm , 2003, Comput. Aided Des..

[17]  Wu Pan,et al.  Application of electronic nose in gas mixture quantitative detection , 2009, 2009 IEEE International Conference on Network Infrastructure and Digital Content.

[18]  J. Watson,et al.  The tin oxide gas sensor and its applications , 1984 .

[19]  Lino Marques,et al.  Application of odor sensors in mobile robotics , 1998 .

[20]  T. Moriizumi,et al.  Analysis of gas sensor transient response by visualizing instantaneous gas concentration using smoke , 1998 .

[21]  Alireza Salehi,et al.  Modeling and simulation of a MOSFET gas sensor with platinum gate for hydrogen gas detection , 2009 .