Research On Heating Surface Fouling Monitoring And Blowing Optimization System Of Coal-fired SCR Denitration Boiler

The selective catalytic reduction (SCR) technology has been widely used in the thermal power industry in China, which has low reaction temperature, high denitrification rate and low ammonia escape rate. With the increasingly severe environmental problems in china, most power plants in china achieve the NOx emission targets with the use of integrated denitrification method for the transformation of coal-fired boilers. Boiler combustion system with tow nitrogen burners, burning ember wind, furnace using selective non catalytic reduction (SNCR), the tail flue by selective catalytic reaction (SCR) turns out multi-level reduction denitration technology. But the SNCR and SCR denitrification process may cause the escape of ammonia, under some certain conditions will generate ammonium hydrogen sulfate. Ammonium hydrogen sulfate is hygroscopicity, corrosive, viscous, stop on the catalytic bed and cause low temperature convective heating surface damage. The main research contents are as follows.1. The mechanism of heated area in the tail of the boiler and the concentration distribution of hydrogen sulfate. Through the analysis of the nature mechanism of ammonium hydrogen sulfate, combined with ammonium hydrogen sulfate at tow temperature at the tail part of the boiler heating surface distribution experiment, to evaluate the SCR denitrification process in tail heating area of ash2. Ash pollution monitoring model. In coal-fired boiler of tow temperature economizer as the object, respectively, using heat transfer coefficient method and the fouling thermal resistance method, establish a tow temperature economizer fouling monitoring model, calculate the economizer cleaning factor, gives two kinds of ash fouling monitoring model calculation example to demonstrate the model accuracy. Finally, the BP neural network model is introduced to predict the cleaning factor of the coal saving device, and the accuracy of the BP neural network model is proved.3. Study on the optimization model of the ash blowing. The most important problem in the process of blowing is to solve the problem of when to blow and how long each time. This paper theoretically establish a blowing ash cycle mathematical model, and combined with the utility of specific circumstances to determine the boiler soot blowing ash equipment. Finally, in order to ensure the safety of the premise established optimal blowing ash system of economic evaluation method.4. A power plant soot blowing optimization test. The experimental results are discussed in depth, and the accuracy of the model is demonstrated. The experimental results show that the system has good control effect on the unit load and variable toad condition.