Coal quality identification method based on electrostatic method combined with long short-term memory neural network

HUANG Xiaobin; YANG Xuan; LIN Kaixiang; XU Qi; LI Yongsheng

doi:10.19666/j.rlfd.202203037

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Coal quality identification method based on electrostatic method combined with long short-term memory neural network

更新时间：2025-09-28

- Coal quality identification method based on electrostatic method combined with long short-term memory neural network
- Thermal Power Generation Vol. 51, Issue 8, Pages: 108-115(2022)
- 作者机构：
  
  1. 华北电力大学控制与计算机工程学院
  2. 清洁高效燃煤发电与污染控制国家重点实验室
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202203037
  CLC： TM621;TP183
- Published：2022
- 稿件说明：
移动端阅览
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.
DOI：

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. DOI： 10.19666/j.rlfd.202203037.

摘要

燃煤机组每台磨煤机的实时入炉煤质对锅炉的燃烧优化调整具有重要意义，但传统煤质检测程序繁琐，且存在检测数据滞后的情况。因此，提出一种基于静电法联合长短时记忆（LSTM）神经网络的入炉煤质辨识方法。根据不同煤粉传输过程中存在静电变化的特性，安装静电传感器检测不同煤粉通过管道时的静电信号强度，结合风煤比、煤粉流速、磨煤机出口温度等影响参数，并结合现场数据分析参数相关性与迟延性，构建煤质辨识LSTM神经网络模型。以某600 MW机组锅炉实测数据为例，经参数寻优后采用LSTM神经网络模型辨识煤质准确率达到86.84%，对4种煤质分类结果的评估指标AUC值均在0.9以上，并与其他机器学习模型进行对比实验，结果表明LSTM神经网络模型具有更高的辨识精度，验证了该方法的可行性和准确性。

Abstract

The ability to obtain the coal quality of each coal mill in real time is of great significance to optimization and adjustment of boiler combustion. The conventional coal quality detection procedure is cumbersome and the detection data lag behind. Therefore

a coal quality identification method based on electrostatic method combined with long short term memory(LSTM) neural network is proposed. In this method

according to the characteristics of electrostatic changes in the transmission process of different pulverized coal

electro-static sensors are installed to detect the electrostatic signal strength of different pulverized coal passing through the pipeline

the correlation and delay of several parameters such as air-coal ratio

pulverized coal flow rate and coall mill outlet temperature is analyzed by combining with the site data

and an LSTM neural network model for coal quality identification is established. Taking the measured data of a 600 MW unit boiler as an example

after parameter optimization

the coal quality identification accuracy of the LSTM neural network model reaches 86.84%

and all the AUC values of the four coal quality classification results are above 0.9. Compared with other machine learning models

the LSTM neural network model has higher identification accuracy

which is feasible and accurate.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Prediction of key indicators of utility boiler based on multi-task uncertainty loss

Prediction model for NO_x emissions from thermal power plants based on long-short-term memory neural network

Application of machine learning in state analysis of power plant equipment

Multi-parameter collaborative prediction model of boilers based on long-short-term memory neural network

LSTM-based performance prediction model for coal mill in a coal-fired unit

Related Author

ZHOU Yong

NIE Xiaoyin

ZHANG Xiaohong

XIE Gang

LI Nan

WANG Yufei

LI Xinli

YANG Guotian

Related Institution

山西华光发电有限责任公司

太原科技大学先进控制与工业智能山西省重点实验室

Xi'an Thermal Power Research Institute Co., Ltd

China Power Investment Corporation Zhuhai Hengqin Thermal Power Co., Ltd

Guangdong Energy Group Co., Ltd. Shajiao C Power Plant

AI问答

⁰