Large combustion power plants pr amount of SO 2 and it is important to hav be able to control and optimize the desulphurization units. Static op insufficient to provide good quanti concentration, and therefore dynamic temperature cycling is studied. The data Linear Discriminant Analysis (LDA) Square (PLS) Regression. SO 2 quan achieved with LDA, even when the ox concentration changes from 4-10%.

Thermal power plants produce SO 2 during combustion of fuel containing sulfur. One way to decrease the SO 2 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 SO 2 , that it is difficult to desorb from the catalyst surface, makes it known as catalyst poison.It is difficult to quantify the SO 2 with static operation, even at the optimum operation temperature of the sensor due to low response levels and saturation already at low concentration of SO 2 . The challenge of SO 2 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 SO 2 . 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% O 2 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 SO 2 into several groups of concentrations up to 2000 ppm was possible. Additionally, PLS analysis indicated a good agreement between the predicted value from the model and the SO 2 concentration from the reference instrument of the pilot plant. 3 IntroductionSO 2 is one of the major air pollutants because it is a precursor of acid rain, forms acid particulates, and is dangerous for human health. However, in a thermal power plant, SO 2 is generally produced when sulfur containing fuel is combusted. In flue gas cleaning processes, SO 2 is usually removed by absorption with lime (CaOH 2 .2H 2 O) or other compounds having high alkalinity. State-of-the-art desulphurization can remove more than 98% of the SO 2 from the flue gas. With increasing environmental concerns, the regulation of SO 2 emission from thermal power plants has become stricter. The installation of sensors in the flue gas duct has been proposed as one of the alternatives to improve the efficiency of the desulphurization un...

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Experiments were performed both in the laboratory and a desulfurization pilot unit in order to improve the SiC-FET sensor performance using two-step data evaluation. In both cases, a porous Pt-gate enhancement type SiC-FET was utilized in a temperature cycled operation (TCO). Liner Discriminant Analysis (LDA) was chosen as the method for multivariate data analysis. Hierarchical methods with two-step LDA worked quite well in the laboratory tests with SO 2 concentrations varied from 25-200 ppm. The same data evaluation was also applied to tests in the desulfurization pilot unit, with higher gas flow and a larger SO 2 concentration range (up to 5000 ppm). The results from the SO 2 quantification showed a significantly improved fit to corresponding reference instrument (FTIR) values. 2 IntroductionSO 2 has a significant impact on human health and the environment due to its ability to cause acid rain and form acid particulates. The majority of the emission comes from the energy sector [1]. The European Environmental Agency (EEA) has reported that in the last decade the SO 2 emission has decreased by about 74% and the majority of the reduction has come from power generation and distribution (58%) [2]. The EEA also stated [2] that the reduction in SO 2 has been the result of use of fuels with lower sulfur content and the increase in efficiency of desulfurization systems. However, these measures are not sufficient. The goal for future SO 2 emission reduction has been outlined according to stricter regulation. One way to achieve this goal is to improve the efficiency of the sulfur removal unit even more.In thermal power plants, SO 2 is produced as an unwanted by-product from the combustion of sulfur containing fuel, and it needs to be removed before venting the flue gas to the atmosphere. SO 2 is removed in a desulfurization system. Lime (Ca(OH) 2 ) is mixed with ash and injected into the dry desulfurization system to react with SO 2 in the gas phase. The resulting products are separated from the flue gas by a fabric filter before venting the flue gas to the atmosphere [3]. In this process, sensors play an important role, not only in detecting the concentration of SO 2 at the outlet of the unit, but also in monitoring the concentration profile throughout the duct. The latter application is important for maintaining concentration uniformity of the flue gas, and thus, for improving the efficiency of the desulfurization unit.SiC based sensors are suitable candidates for the desulfurization application due to the inert behavior of SiC in high temperature, oxidizing environments [4][5][6]. Moreover, SiC-FET sensors have shown promising results in industrial environments for CO [7,8] and NH 3 [6,9].The sensors are based on commercial transistor devices with porous catalytic metal gate on top of the gate oxide [4]. The target gas is adsorbed, and then decomposed upon interaction with oxygen ions on the catalytic metal surface. The interaction between the target gases and the metal surface changes the balance of the surf...

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