Sic-fet Gas Sensors Developed for Control of the Flue Gas Desulfurization System in Power Plants, Experimental and Modeling

Electricity and power generation is an essential part of our life. However, power generation activities also create by-products (such as sulphur oxides, nitrogen oxides, carbon monoxide, etc), which can be dangerous when released to the atmosphere. Sensors, as part of the control system, play very vital role for the flue gas cleaning processes in power plants. This thesis concerns the development of Silicon Carbide Field Effect Transistor (SiC-FET) gas sensors as sensors for sulfur containing gases (SO2 and H2S) used as part of the environmental control system in power plants. The works includes sensor deposition and assembly, sensing layer characterization, operation mode development, performance testing of the sensors in a gas mixing rig in the laboratory and field test in a desulfurization pilot unit, and both experimental and theoretical studies on the detection mechanism of the sensors. The sensor response to SO2 was very small and saturated quickly. SO2 is a very stable gas and therefore reaction with other species requires a large energy input. SO2 mostly reacts with the catalyst through physisorption, which results in low response level. Another problem was that once it finally reacted with oxygen and adsorbed on the surface of the catalyst in form of a sulfate compound, it is desorbed with difficulty. Therefore, the sensor signal saturated after a certain time of exposure to SO2. Different gate materials were tested in static operation (Pt, Ir, Au), but the saturation phenomena occurred in all three cases. Dynamic sensor operation using temperature cycling and multivariate data analysis could mitigate this problem. Pt-gate sensors were operated at several different temperatures in a cyclic fashion. One of the applied temperatures was chosen to be very high for a short time to serve as cleaning step. This method was also termed the virtual multi sensor method because the data generated could represent the data from multiple sensors in static operation at different temperatures. Then, several features of the signal, such as mean value and slope, were extracted and processed with multivariate data analysis. Linear Discrimination Analysis (LDA) was chosen since it