摘要
针对某电厂300 MW机组低NO_x燃烧优化调整,结合NO_x生成规律与数据分析方法,筛选原始运行数据库,建立二次风软测量模型与氧量聚类挖掘模型,从而减少原始运行参数数量,并利用粒子群优化Apriori算法,挖掘精简后的数据库中符合机组NO_x减排要求的各个参数的最优参考工况.结果表明:寻优后的烟气含氧量、磨煤机组合方式、二次风门开度和分离燃尽风(SOFA)门开度等参数均符合燃烧调整试验结论,寻优结果在保证锅炉效率的前提下,有效地降低了SCR装置进口NO_x质量浓度.
To satisfy the low-NOx combustion adjustment requirement of a 300 MW unit, a soft measure- ment model of secondary air and a clustering model of oxygen content in the flue gas were built up based on the NOx formation rules and data analysis method by filtering the data in original database of the power plant to reduce the number of operating parameters, after which various parameters in the simplified data- base were optimized using Aprior algorithm improved with particle swarm optimization, so as to achieve the reduction of NOx emission. Results show that all the optimized parameters are in accordance with the combustion adjustment test, such as the oxygen content in flue gas, combination mode of coal mills, and opening degree of both the secondary air damper and SOFA air damper, etc. , indicating that the NOx concentration can be effectively reduced at the inlet of SCR facility on the premise of ensuring the thermal efficiency of boiler.
出处
《动力工程学报》
CAS
CSCD
北大核心
2016年第5期337-342,403,共7页
Journal of Chinese Society of Power Engineering
关键词
燃烧优化
数据挖掘
关联规则
combustion optimization
data mining
NOx
association rule