TiO2-based sensor arrays modeled with nonlinear regression analysis for simultaneously determining CO and O2 concentrations at high temperatures

Responses of TiO2-based sensor arrays were analyzed using kernel ridge regression modeling to determine the concentrations of CO and O2 in gas mixtures at 873 K. Two variations of a two-sensor combination were studied. In each array, a La2O3-doped TiO2 sensor was used, whereas the second sensor in the array was a CuO–La2O3-doped TiO2 sensor, doped with different levels of copper. In sensor array I, 2 wt.% CuO was used, while 8 wt.% CuO was used in the second. Sensor array I was used to demonstrate the kernel ridge regression methodology. The concept of orthogonality of sensors was developed, which is a quantitative measure of how well the sensor array can discriminate between the two gases of interest. This model was then used to extract the concentrations of CO and O2 in a gas mixture over ranges of 2–10% O2 and 250–1000 ppm CO using the second sensor array. Prediction ability was found to be reasonable over certain concentration ranges and was determined by the orthogonality of the sensor responses.