EN基于煤仓分层和软测量的入炉煤实时监测
Real-time Monitoring of Furnace Feed Coal Based on Layering Model and Soft Measurement of Coal Bunker
- 动力工程学报 2025年45卷第2期 页码：190-197
- 作者机构：
1. 浙江大学 能源高效清洁利用全国重点实验室,浙江,杭州,310027
2. 浙江浙能台州第二发电有限责任公司,浙江,台州,317109
3. 杭州集益科技有限公司,浙江,杭州,310012
- 作者简介：
[ "陈亚平(1999—),男,四川绵阳人,硕士研究生,研究方向为智能燃料" ]
- 基金信息：
DOI：10.19805/j.cnki.jcspe.2025.230758
中图分类号： TM621-
网络出版：2025-02-15，
纸质出版：2025
- 稿件说明：
移动端阅览
陈亚平,王焕明,孙胡彬,魏勇,王灵敏,赵敏,周晓亮,徐明祥,赵虹. 基于煤仓分层和软测量的入炉煤实时监测动力工程学报, 2025, 45(2): 190-197 https://doi.
org/10.19805/j.cnki.jcspe.2025.230758
陈亚平,王焕明,孙胡彬,魏勇,王灵敏,赵敏,周晓亮,徐明祥,赵虹. 基于煤仓分层和软测量的入炉煤实时监测动力工程学报, 2025, 45(2): 190-197 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.230758.
org/10.19805/j.cnki.jcspe.2025.230758 DOI：
摘要
为了实现入炉煤的实时监测
在燃料唯一特征码全过程跟踪模型的基础上构建了煤仓分层模型
初步粗略地监测到煤种分层情况
并根据水分质量分数软测量结果和煤仓分层模型进行水分质量分数的匹配
将成功匹配的历史数据作为入炉煤煤种的真实值
将磨煤机电流、出力等运行参数作为特征输入
建立了基于随机森林算法的入炉煤预测模型
结合模型预测结果和水分质量分数软测量结果对煤仓分层模型错误部分进行了修正。结果表明:分类模型评估指标的测试结果为0.880
满足工程需求;所提出的将机理分析与机器学习相结合的燃煤信息监测模型实现了入炉煤的全时段识别
为锅炉燃烧优化及智能锅炉的建设打下基础
同时实现了燃料特征码全过程跟踪的闭环。
Abstract
In order to achieve real-time monitoring of coal fed into the furnace
a coal bunker layering model was developed based on the tracking model of unique fuel feature code throughout the entire process. The coal type layering situation was roughly monitored
and the matching of moisture contents was performed based on the soft measurement results of moisture content and the coal bunker layering model. The successfully matched historical data were used as the true coal type entering the furnace
and the operating parameters such as the current and output of the coal mills were taken as feature inputs. The prediction model of the furnace feed coal based on the random forest algorithm was developed
and the erroneous parts of the coal bunker layering model were corrected by combining the model prediction results and moisture soft measurement results. Results show that test result of evaluation indicators for classification models is 0.880
which meets the engineering requirements. The proposed coal information monitoring model based on mechanism analysis and machine learning can achieve full-time identification of furnace feed coal. It lays a foundation for combustion optimization and the construction of intelligent boilers
and also realizes the closed loop of the whole-process tracking of fuel feature code.
关键词
Keywords
references
YIN Junjie, LIU Ming, ZHAO Yongliang, et al. Dynamic performance and control strategy modification for coal-fired power unit under coal quality variation[J]. Energy, 2021, 223: 120077.
GUO Lianbo, ZHANG Deng, SUN Lanxiang, et al. Development in the application of laser-induced breakdown spectroscopy in recent years: a review[J]. Frontiers of Physics, 2021, 16(2): 22500.
LIU Ke, HE Chao, ZHU Chenwei, et al. A review of laser-induced breakdown spectroscopy for coal analysis[J]. TrAC Trends in Analytical Chemistry, 2021, 143: 116357.
SHETA S, AFGAN M S, HOU Zongyu, et al. Coal analysis by laser-induced breakdown spectroscopy: a tutorial review[J]. Journal of Analytical Atomic Spectrometry, 2019, 34(6): 1047-1082.
于鹏峰, 苏攀, 刘佳薇, 等. 基于PL-Raman光谱分析的煤质快速检测方法[J]. 动力工程学报, 2022, 42(3): 215-220. YU Pengfeng, SU Pan, LIU Jiawei, et al. Rapid evaluation method of coal property using PL-Raman spectroscopy[J]. Journal of Chinese Society of Power Engineering, 2022, 42(3): 215-220.
POTGIETER-VERMAAK S, MALEDI N, WAGNER N, et al. Raman spectroscopy for the analysis of coal: a review[J]. Journal of Raman Spectroscopy, 2011, 42(2): 123-129.
赵龙. 应用PGNAA技术检测烧结混合料元素含量的研究[D]. 长春: 吉林大学, 2021.
ZOLFAGHARI M, MASOUDI S F, RAHMANI F, et al. Thermal neutron beam optimization for PGNAA applications using Q-learning algorithm and neural network[J]. Scientific Reports, 2022, 12(1): 8635.
李雄威, 王哲, 刘汉强, 等. 基于激光诱导击穿光谱的燃煤热值定量分析[J]. 红外与激光工程, 2017, 46(7): 0734001. LI Xiongwei, WANG Zhe, LIU Hanqiang, et al. Quantitative analysis of heat value of coal by laser-induced breakdown spectroscopy[J]. Infrared and Laser Engineering, 2017, 46(7): 0734001.
LI Wenbing, LU Jidong, DONG Meirong, et al. Quantitative analysis of calorific value of coal based on spectral preprocessing by laser-induced breakdown spectroscopy (LIBS)[J]. Energy & Fuels, 2018, 32(1): 24-32.
成艳亭, 宋立信, 池锋, 等. 入炉煤质在线软测量技术研究与应用进展[J]. 洁净煤技术, 2021, 27(5): 38-51. CHENG Yanting, SONG Lixin, CHI Feng, et al. Development and application of soft-measurement technology for online monitoring of coal quality in power generation[J]. Clean Coal Technology, 2021, 27(5): 38-51.
YIN Xianhui, NIU Zhanwen, HE Zhen, et al. Ensemble deep learning based semi-supervised soft sensor modeling method and its application on quality prediction for coal preparation process[J]. Advanced Engineering Informatics, 2020, 46: 101136.
孙胡彬, 杨建国, 金宏伟, 等. 基于贝叶斯优化—随机森林回归的燃煤锅炉NOx预测模型[J]. 动力工程学报, 2023, 43(7): 910-916. SUN Hubin, YANG Jianguo, JIN Hongwei, et al. NOx prediction model for coal-fired boiler based on bayesian optimization-random forest regression[J]. Journal of Chinese Society of Power Engineering, 2023, 43(7): 910-916.
巨林仓, 李磊, 赵强. 基于遗传神经网络的锅炉入炉煤质软测量研究[J]. 热力发电, 2011, 40(3): 24-27. JU Lincang, LI Lei, ZHAO Qiang. Study on soft-measurement of quality for furnace-entering coal based on genetic neural network[J]. Thermal Power Generation, 2011, 40(3): 24-27.
GAO Yaokui, ZENG Deliang, LIU Jizhen. Modeling of a medium speed coal mill[J]. Powder Technology, 2017, 318: 214-223.
高天龙. 锅炉入炉煤质跟踪识别技术的研究[D]. 北京: 华北电力大学, 2021.
黄孝彬, 杨萱, 林锴翔, 等. 基于静电法联合长短时记忆神经网络的入炉煤质辨识方法[J]. 热力发电, 2022, 51(8): 108-115. HUANG Xiaobin, YANG Xuan, LIN Kaixiang, et al. Coal quality identification method based on electrostatic method combined with long short-term memory neural network[J]. Thermal Power Generation, 2022, 51(8): 108-115.
刘福国. 电站锅炉入炉煤水分实时监测的研究[J]. 锅炉技术, 2003, 34(6): 12-14. LIU Fuguo. Investigation in moisture on-line monitoring for utility boiler firing coal basing on milling system operation parameters measurement[J]. Boiler Technology, 2003, 34(6): 12-14.
常太华, 常建平, 田亮, 等. 入炉煤水分软测量模型的现场应用[J]. 电力科学与工程, 2006(4): 52-55. CHANG Taihua, CHANG Jianping, TIAN Liang, et al. Application of soft sensing model in moisture measurement of feeding coal[J]. Electric Power Science and Engineering, 2006(4): 52-55.
李锋, 罗嘉, 赵征, 等. 锅炉入炉煤水分质量分数在线软测量[J]. 动力工程学报, 2014, 34(7): 512-517. LI Feng, LUO Jia, ZHAO Zheng, et al. Online soft sensing of coal moisture for utility boilers[J]. Journal of Chinese Society of Power Engineering, 2014, 34(7): 512-517.
魏勇, 江学文, 寿志杰. 燃煤电厂煤仓动态监测及其在智能燃料系统中的应用[J]. 中国设备工程, 2020(18): 151-153. WEI Yong, JIANG Xuewen, SHOU Zhijie. Dynamic monitoring of coal bunkers in coal-fired power plants and its application in intelligent fuel systems[J]. China Plant Engineering, 2020(18): 151-153.
江学文, 魏勇, 陈永辉, 等. 一种煤仓煤种分层实时监测方法: 201911066824.8[P]. 2021-05-11.
DONOHO D L, JOHNSTONE I M. Adapting to unknown smoothness via wavelet shrinkage[J]. Journal of the American Statistical Association, 1995, 90(432): 1200-1224.
0
浏览量
29
下载量
0
CSCD
- 网络PDF下载
- Meta-XML下载
- BibTeX下载
- Endnote下载
- RefMan 下载
- NoteExpress下载
- NoteFirst下载
关联资源
相关文章
相关作者
相关机构
AI问答- 技术支持由北京北大方正电子有限公司提供 京ICP备09064830号-19
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies