Research On Optimization Strategy Of Desulfurization System Operation Based On Association Rules

Energy saving and emission reduction is an important strategy for environmental protection and sustainable development.Thermal power plants are major consumers of primary energy.Coal combustion emits a large amount of SO2,NOx,dust and other pollutants into the atmosphere.In order to reduce SO2 emissions,coal-fired units have installed a desulfurization system,which increases a lot of energy and material consumption during the operation of the system.Therefore,it is of great significance to optimize the desulfurization system and main power-consuming equipment.Firstly,a detailed analysis of the wet desulfurization process is made,and the degree of influence of each factor on the desulfurization efficiency is analyzed from the three aspects of equipment,absorbent and operating factors.The characteristics of the historical operating data of the desulfurization system are analyzed,the desulfurization parameters are classified,and the main parameters that affect the desulfurization efficiency are regressed,and the optimal operating range of the main parameters that affect the desulfurization efficiency is determined.Secondly,the algorithm is improved to solve the problems of long running time,high storage space requirements,and frequent output itemsets in the classic Apriori association rule algorithm.The improved K-means clustering partition is used to replace the generation process of frequent itemsets in association rules,which shortens the running time,and there is no output of frequent itemsets,which greatly reduces the storage space requirements.The improved algorithm can quickly and efficiently partition the input parameters to form a working condition library when facing massive data,which effectively improves the operating efficiency of the mining algorithm.Based on the improved algorithm,a visual interface for data analysis and processing is written.Finally,quantitative analysis is used to analyze historical operating data to obtain the optimal p H value and liquid level in actual operation.Apply the improved Apriori association rule algorithm based on K-means to the desulfurization system,aiming at the lowest power consumption,and analyze the optimization of the slurry circulating pump of the main power-consuming equipment,and draw the pump with relatively low power consumption under various working conditions.The combination of and the operating frequency of the variable frequency pump.It was compared with historical operating data to verify the validity of the results.This paper uses exponential smoothing method to predict the input parameters,and assists the mining results to form an optimization guide table to adjust the operating parameters and the slurry circulation pump.Research shows that the improved Apriori association rule algorithm based on k-means clustering can quickly and accurately mine the combination of slurry circulating pumps and the frequency of variable frequency pumps under the target parameters,and the mining results are practical and can effectively improve the economic benefits of power plants.