摘要
为了更好地利用电厂的运行数据设计锅炉在线燃烧优化系统,对电厂的锅炉运行数据进行分工况建模。利用K均值聚类方法对静态数据集进行工况划分,根据误差平方和曲线确定聚类数,最终将锅炉工况按照负荷分为了4类,按照相对煤质系数分为了3类,共获得了12个建模数据集。利用神经网络模型对各工况的数据集进行单独建模,建模结果显示分工况建模后整体的精度得到了提高。利用遗传算法对建立的模型进行搜索优化,赋予锅炉效率与NO_(x)排放浓度不同权重,获得了不同优化目标下锅炉运行数据的推荐值,多目标优化结果表明NO_(x)排放浓度可以在锅炉效率的较小损失前提下获得大幅下降,优化结果与机理分析一致,可供电厂操作人员参考。
In order to make better use of the operation data of the power plant to design the boiler on-line combustion optimization system,the boiler operation data of the power plant is modeled by different working conditions.The static data set is divided into working conditions by K-means clustering method,and the number of clusters is determined according to the squared error curve.Finally,the boiler working conditions are divided into 4 categories according to the load,and 3 categories according to the relative coal quality coefficient.A total of 12 modeling datasets were obtained.The neural network model is used to model the data sets of each working condition separately,and the modeling results show that the overall accuracy is improved after modeling by working conditions.The established model was searched and optimized by genetic algorithm,and different weights were given to boiler efficiency and NO_(x)emission concentration,and the recommended values of boiler operation data under different optimization objectives were obtained.The multi-objective optimization results show that the NO_(x)emission concentration can be greatly reduced under the premise of a small loss of boiler efficiency.The optimization results are consistent with the mechanism analysis and can be used for reference by power plant operators.
作者
曹歌瀚
黄亚继
徐文韬
陈波
王新宇
张荣初
刘宇清
邹怡然
CAO Gehan;HUANG Yaji;XU Wentao;CHEN Bo;WANG Xinyu;ZHANG Rongchu;LIU Yuqing;ZOU Yiran(Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education,Southeast University,Nanjing 210096,China;Jiangsu Frontier Electric Power Technology Co.,Ltd.,Nanjing 211102,China;Nanjing Changrong Acoustic Inc.,Nanjing 210008,China)
出处
《锅炉技术》
北大核心
2023年第5期41-47,共7页
Boiler Technology
基金
江苏省科技成果转化专项资金项目(BA2020001)。
