Calibration of MOX gas sensors in open sampling systems based on Gaussian Processes

Abstract

Calibration of metal oxide (MOX) gas sensors for continuous monitoring is a complex problem due to the highly dynamic characteristics of the gas sensor signal when exposed to a natural environment in an Open Sampling System (OSS). This work presents a probabilistic approach to the calibration of MOX gas sensors using Gaussian Processes (GP). The proposed approach estimates for every sensor measurement a probability distribution of the corresponding gas concentration, which enables the calculation of confidence intervals for the predicted concentrations. Being able to predict the uncertainty about the concentration related to a particular sensor response is particularly advantageous in OSS applications where typically many sources of uncertainty exist. The proposed approach has been tested with an experimental setup where an array of MOX sensors and a Photo Ionization Detector (PID) are placed downwind with respect to the gas source. The PID is used to obtain ground truth concentration measurements. Comparison with standard calibration methods demonstrate the advantage of the proposed approach.