Vahid Ghafarinia - K. N. Toosi University of Technology, Tehran, Iran
Mehdi Aliagha Sarghamish
Faramarz Hossein-Babaei

Responses of chemoresistive gas sensors suffer from the influences of the variations of the ambient humidity and temperature. An appropriatecountermeasure is required if any qualitative andquantitative analysis is going to be implemented based on these responses. Here, a novel compensation methodbased on the neuro-fuzzy modelling of the sensor behaviour is presented. Gas sensor is treated as a nonlinear system that is affected simultaneously by three inputs, the partial pressure of the target gas and the humidity and the temperature of the surrounding atmosphere. The single output of this system is thesensor’s resistance that is referred to as the sensor response. A large database was created out of theexperimental results, i.e. the inputs and outputs of the system in different conditions. It was shown that an appropriate neuro-fuzzy model can be employed formodelling of the system and a quantitative analysis of the responses in different conditions. The results of the analysis were employed for the partial compensation of the drift caused errors. The method reduced the humidity and temperature variation caused errors by a factor of 4 in the laboratory tests conducted.

Neuro-fuzzy modelling, Chemoresistive gas sensor, drift compensation, humidity and temperature effects, quantitative analysis