Real-time Monitoring of Furnace Feed Coal Based on Layering Model and Soft Measurement of Coal Bunker

CHEN Yaping; WANG Huanming; SUN Hubin; WEI Yong; WANG Lingmin; ZHAO Min; ZHOU Xiaoliang; XU Mingxiang; ZHAO Hong

doi:10.19805/j.cnki.jcspe.2025.230758

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Real-time Monitoring of Furnace Feed Coal Based on Layering Model and Soft Measurement of Coal Bunker

更新时间：2026-01-21

- Real-time Monitoring of Furnace Feed Coal Based on Layering Model and Soft Measurement of Coal Bunker
- Chinese Journal of Power Engineering Vol. 45, Issue 2, Pages: 190-197(2025)
- 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：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.230758
  CLC： TM621
- Published Online：15 February 2025，
  
  Published：2025
- 稿件说明：
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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.

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references

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