Prediction model of main steam temperature of coal-fired boiler considering time delay characteristic optimization

YANG Chunlai; YUAN Xiaolei; YIN Zhe; JIN Fei; LI Jianfeng; WU Bin; LIU Xuelai

doi:10.19666/j.rlfd.202202053

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Prediction model of main steam temperature of coal-fired boiler considering time delay characteristic optimization

更新时间：2025-09-28

- Prediction model of main steam temperature of coal-fired boiler considering time delay characteristic optimization
- Thermal Power Generation Vol. 51, Issue 8, Pages: 124-129(2022)
- 作者机构：
  
  1. 国网河北能源技术服务有限公司
  2. 华北电力大学控制与计算机工程学院
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202202053
  CLC： TM621.2
- Published：2022
- 稿件说明：
移动端阅览
YANG Chunlai, YUAN Xiaolei, YIN Zhe, et al. Prediction model of main steam temperature of coal-fired boiler considering time delay characteristic optimization[J]. Thermal power generation, 2022, 51(8): 124-129.
DOI：

YANG Chunlai, YUAN Xiaolei, YIN Zhe, et al. Prediction model of main steam temperature of coal-fired boiler considering time delay characteristic optimization[J]. Thermal power generation, 2022, 51(8): 124-129. DOI： 10.19666/j.rlfd.202202053.

摘要

构建精确的锅炉主蒸汽温度预测模型有利于提高其控制品质，考虑锅炉运行参数的时延特性对主蒸汽温度的预测精度具有较大影响。使用长短时记忆（LSTM）神经网络算法构建模型预测锅炉主蒸汽温度变化趋势，并针对锅炉运行参数时延特性的问题，提出利用离散粒子群算法实现网络模型输入变量时滞的优化。最后，基于某1 000 MW燃煤锅炉的历史数据，验证时延特性优化后的主蒸汽温度预测模型。预测结果表明，该模型预测均方根误差为0.47℃，较传统方法构建的LSTM神经网络模型预测误差降低6%，预测精度更高。

Abstract

Building an accurate prediction model of boiler main steam temperature is conducive to improving its control quality. The time delay characteristics of boiler operation parameters have a great impact on the prediction accuracy of main steam temperature. Long-short term memory(LSTM) neural network algorithm is adopted to establish a model to predict the change trend of boiler main steam temperature

and aiming at solving the problem of determining the time delay characteristics of boiler operation parameters

discrete particle swarm optimization algorithm is used to optimize the time delay of network model input variables. Moreover

based on the historical data of a 1 000 MW coal-fired boiler

the main steam temperature prediction model optimized by time delay characteristics is verified. The results show that

the prediction error of the optimized LSTM network model considering the time delay characteristics is 0.47 ℃

namely 6% smaller than that of the conventional LSTM model

indicating it has a higher prediction accuracy.

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

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

CLOSED-LOOP CONTROL SYSTEM OF CFB BOILERS

DIANOSTIC ANALYSIS OF SOMEWHAT LOWER TEMPERATURE PROBLEM FOR MAIN STEAM OF BOILER NO.5 IN DATANG TAIYUAN COGENERATION POWER PLANT NO.2

Related Author

WANG Yufei

LI Nan

XIE Gang

ZHANG Xiaohong

NIE Xiaoyin

ZHOU Yong

YANG Guotian

ZHANG Tao

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

State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology

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

Daliuta Thermal Power Plant

AI问答

⁰