基于数据驱动的NO_(x)稳定性评价

Data-driven NO_(x) Stability Evaluation

燃煤机组烟气NO_(x)浓度的稳定性影响脱硝系统的运行性能。通过构建稳定性评估模型,实现对NO_(x)浓度大数据分布的量化评价。构建了K均值聚类与3σ准则相结合的原始大数据清洗方法,通过滑动窗格法划分不同工况数据。基于最小二乘回归,建立了NO_(x)浓度基准值模型。基于偏离程度,构建了偏离度函数和稳定系数函数,归一化处理后得到稳定性评分函数,从而实现全负荷NO_(x)稳定性定量评价。利用该模型对某660 MW燃煤机组NO_(x)数据进行评估,结果表明:NO_(x)浓度分布整体集中度高,与负荷率呈正相关,变负荷工况稳定性得分比稳定工况低2.35%左右;将负荷率从50%提升至100%,稳定性得分提升了21.28%,说明所构建的模型能有效评价NO_(x)分布的稳定性,为机组对标评价提供了依据。

The stability of NO_(x) concentration in a coalfired unit affects the operation performance of denitrification system.By constructing the stability evaluation model,the quantitative evaluation of the big data distribution of NO_(x) concentration was realized.The original big data cleaning method combining K-means clustering and 3σcriterion was constructed,and the data of different working conditions were divided by the sliding window method.A NO_(x) concentration reference value model was established based on the leastsquares regression method.Based on the deviation degree,the deviation degree function and the stability coefficient function were constructed,and the stability scoring function was obtained after normalization,so as to realize the quantitative evaluation of the stability of full load NO_(x).The model was used to evaluate NO_(x) data from a 660 MW coal-fired unit.The results show that the overall concentration of NO_(x) concentration distribution is high and positively correlated with load rate.The stability score of variable load condition is about 2.35%lower than that of stable condition.When the load rate is increased from 50%to 100%,the stability score is increased by 21.28%.It shows that the model can effectively evaluate the stability of NO_(x) distribution and provide a basis for benchmarking coal-fired units.