SiC–FET based SO2 sensor for power plant emission applications

Abstract

Thermal power plants produce SO2 during combustion of fuel containing sulfur. One way to decrease the SO2 emission from power plants is to introduce a sensor as part of the control system of the desulphurization unit. In this study, SiC–FET sensors were studied as one alternative sensor to replace the expensive FTIR (Fourier Transform Infrared) instrument or the inconvenient wet chemical methods. The gas response for the SiC–FET sensors comes from the interaction between the test gas and the catalytic gate metal, which changes the electrical characteristics of the devices. The performance of the sensors depends on the ability of the test gas to be adsorbed, decomposed, and desorbed at the sensor surface. The feature of SO2, that it is difficult to desorb from the catalyst surface, makes it known as catalyst poison. It is difficult to quantify the SO2 with static operation, even at the optimum operation temperature of the sensor due to low response levels and saturation already at low concentration of SO2. The challenge of SO2 desorption can be reduced by introducing dynamic operation in a designed temperature cycle operation (TCO). The intermittent exposure to high temperature can help to desorb SO2. Simultaneously, additional features extracted from the sensor data can be used to reduce the influence of sensor drift. The TCO operation, together with pattern recognition, may also reduce the baseline and response variation due to changing concentration of background gases (4–10% O2 and 0–70% RH), and thus it may improve the overall sensor performance. In addition to the laboratory experiment, testing in the desulphurization pilot unit was performed. Desulphurization pilot unit has less controlled environment compared to the laboratory conditions. Therefore, the risk of influence from the changing concentration of background gas is higher. In this study, linear discriminant analysis (LDA) and partial least square (PLS) were employed as pattern recognition methods. It was demonstrated that using LDA quantification of SO2 into several groups of concentrations up to 2000ppm was possible. Additionally, PLS analysis indicated a good agreement between the predicted value from the model and the SO2 concentration from the reference instrument of the pilot plant.