基于KPCA-Kmeans++数据挖掘的二次风燃烧优化

Secondary Air Combustion Optimization Based on KPCA-Kmeans++Data Mining

为了解决算法对全工况数据挖掘难以精简寻优,以及针对燃烧优化调整试验中工况点选取不全面的问题,设计了一种基于KPCA-Kmeans++数据挖掘燃烧优化方法。通过对海量历史运行数据挖掘,发现不同工况下锅炉稳态运行条件下主要运行参数与性能指标之间的关联关系,并根据关联规律得到锅炉高效率低污染运行时所对应的参数运行目标值,通过对大量数据挖掘结果进行降维聚类得到直观、精简的燃烧控制规律,进而指导燃烧优化。以起磨方式作为工况划分标准,采用核主成分分析(KPCA)对历史数据所包含参数进行降维处理,以改进K均值(Kmeans++)算法对同种工况下海量数据聚类分析,在每种工况下确定3种燃烧优化模式。对比某电厂5号机组二次风优化效果表明,优化后SCR脱销入口NOx浓度平均下降29.2mg/m 3,锅炉效率平均提升0.11%。

In order to simplify the complicated process of data mining for power plants by various algorithms,and to solve the problem of incomplete selection of operating conditions in the combustion optimization adjustment test,a combustion optimization method based on KPCA-Kmeans++data mining was designed.Through the mining of massive historical operating data,the correlation between the main operating parameters and performance indicators of the boiler under steady-state operating conditions in different operating conditions was found,and the operating target values of the parameters corresponding to the boiler’s high-efficiency and low-pollution operation were obtained according to the correlation law.By performing dimensionality reduction clustering on a large amount of data mining results,an intuitive and streamlined combustion control law was obtained,so as to guide combustion optimization.Regarding the grinding method as the working condition division standard,the kernel principal component analysis(KPCA)was used to reduce the dimensionality of the parameters contained in the historical data,thus improving the performance of Kmeans++algorithm in cluster analysis of massive data under the same working condition.Under each working condition,three combustion optimization modes were determined.Comparing the secondary air optimization effect of Unit 5 in a certain power plant,the NOx concentration at the inlet of the SCR(Selective Catalytic Reduction)device after the optimization decreased by 29.2mg/m 3 on average,and the boiler efficiency increased by 0.11%on average.