Research On Low NO_x Emission Control Of Power Plant Boiler Based On Data Mining

In recent years,China's air pollution is serious,more often appear haze weather.Thermal power plant is one of the important sources of pollution,the emission of NO_x is an important component of atmospheric pollutants,so the study of power plant boiler low NO_x emission control method is important.The paper proposes to optimize the parameters of boiler combustion by using data mining technology,in order to achieve the goal of reducing NO_x in the boiler.The main contents are as follows:(1)The formation mechanism of NO_x in power station coal fired boiler is introduced.The method of controlling NO_x emission reduction and the main factors influencing the NO_x emission of boiler are described in detail.(2)The rough set theory is introduced in detail,and the optimal parameters related to the NO_x emission of the boiler are obtained by using the attribute reduction algorithm of rough set.(3)The association rules mining algorithm and Apriori algorithm are described in detail.CA(competitive agglomeration)algorithm is introduced into the fuzzy association rule,and the rules knowledge for guiding the low NO_x emission of the boiler is excavated.The result of excavation shows that it is beneficial to suppress the formation of NO_x in the boiler by reducing the oxygen content of the flue gas and reducing the air flow in the primary air and in the middle&upper secondary air and increasing the down air volume of the secondary air and the tertiary air.(4)Using the existing LS-SVM boiler NO_x emission mathematical prediction model to test the guiding effect of mining rules.The results show that the fuzzy correlation rules of mining can optimize the parameters of the boiler,and the NO_x emission of the boiler can be reduced remarkably.Which indicates that the proposed scheme is feasible and effective.