浙江电力

2022, v.41;No.317(09) 86-94

[打印本页] [关闭]
本期目录(Current Issue) | 过刊浏览(Archive) | 高级检索(Advanced Search)

基于落渣图像的煤粉锅炉炉内结渣监测试验研究
Experimental study on slagging monitoring of pulverized coal-fired boiler based on slagging image

林国辉,朱静,姜国强,钱芳树,陈晓玮,周永刚,黄群星
LIN Guohui,ZHU Jing,JIANG Guoqiang,QIAN Fangshu,CHEN Xiaowei,ZHOU Yonggang,HUANG Qunxing

摘要(Abstract):

炉内结渣会影响锅炉运行的安全和效率,精确并及时投用吹灰器是避免炉内结渣恶化的有效措施。为实现炉内结渣的实时监测以指导吹灰器的使用,提出一种基于落渣图像的煤粉锅炉炉内结渣监测方法。通过在实验室搭建炉内落渣冷态模拟实验台,分析发现利用渣块轨迹的长宽比、亮度特征结合大数据统计的方法可以提高结渣监测的准确性,采用相邻两帧图像相减的方法能够比较准确地提取渣块轨迹。采用660 MW机组锅炉冷灰斗处布置落渣图像实时监测系统,对记录的落渣图像进行处理,计算渣块大小、落渣位置和亮度等参数,统计炉内落渣规律。结果表明:锅炉降负荷时,根据落渣图像可判断出落渣量增加,这与炉底捞渣机油压的上升趋势一致,验证了监测系统测量结果的准确性。
Furnace slagging affects operation safety and efficiency of boilers. The accurate and timely use of a sootblower can prevent the furnace slagging deterioration. To instantaneously monitor the furnace slagging to guide the use of the sootblower,a slagging monitoring method in pulverized coal boiler based on slagging image is proposed. A cold state test bench for slag simulation is built in the laboratory. It is found through analysis that the accuracy of slagging monitoring can be improved by use of length-width ratios and brightness characteristics of slag blocks,and big data statistics,and that subtraction of two adjacent frames of pictures can help extract trajectories of the slag blocks. A real-time slag image monitoring system is equipped at the cold ash hopper of a 660 MW boiler to process the recorded slagging image,calculate the slag size,location,and brightness to grasp the rule of the slags in the furnace. The results show that when the load is lowered,the slag amount increases according to the real-time monitoring system,which is consistent with the oil pressure rise of the slag conveyor at the bottom of the furnace,which verifies the accuracy of the results of the monitoring system.

关键词(KeyWords): 煤粉锅炉;渣块轨迹;落渣大小;落渣位置;结渣监测
pulverized coal-fired boiler;slag trajectory;slag size;slag location;slagging monitoring

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划课题(2018YFC1901304)

作者(Author): 林国辉,朱静,姜国强,钱芳树,陈晓玮,周永刚,黄群星
LIN Guohui,ZHU Jing,JIANG Guoqiang,QIAN Fangshu,CHEN Xiaowei,ZHOU Yonggang,HUANG Qunxing

DOI: 10.19585/j.zjdl.202209011

参考文献(References):

扩展功能
本文信息
服务与反馈
本文关键词相关文章
本文作者相关文章
中国知网
分享