Prediction model for NO<sub>x</sub> emissions from thermal power plants based on long-short-term memory neural network

YANG Guotian; ZHANG Tao; WANG Yingnan; LI Xinli; LIU He

doi:10.19666/j.rlfd.201805113

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

更新时间：2025-09-28

- Prediction model for NO_x emissions from thermal power plants based on long-short-term memory neural network
- Thermal Power Generation Vol. 47, Issue 10, Pages: 12-17(2018)
- 作者机构：
  
  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.201805113
  CLC： X511;TK323
- Published Online：28 September 2018，
  
  Published：2018
- 稿件说明：
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YANG Guotian, ZHANG Tao, WANG Yingnan, et al. Prediction model for NO_x emissions from thermal power plants based on long-short-term memory neural network[J]. Thermal power generation, 2018, 47(10): 12-17.
DOI：

YANG Guotian, ZHANG Tao, WANG Yingnan, et al. Prediction model for NO_x emissions from thermal power plants based on long-short-term memory neural network[J]. Thermal power generation, 2018, 47(10): 12-17. DOI： 10.19666/j.rlfd.201805113.

摘要

火电厂燃煤锅炉产生的NO_x是大气污染物的重要来源之一

建立有效的NO_x排放预测模型是降低NO_x排放的基础。针对火电厂控制系统数据的海量化和高维化及燃煤锅炉多参数多变量相互耦合的特点

首先利用主成分分析法对火电厂分布式控制系统(DCS)数据进行特征提取

消除各特征变量间的耦合性;然后将提取的特征作为长短期记忆(LSTM)神经网络的输入

得到火电厂NO_x排放预测模型。将该模型与传统循环神经网络(RNN)模型、最小二乘支持向量机(LSSVM)模型应用于某超超临界660 MW机组燃煤锅炉对NO_x排放质量浓度进行预测。结果表明:LSTM神经网络和RNN模型预测效果均优于LSSVM模型;本文提出的LSTM神经网络模型预测准确率达到79%

均方根误差为0.398

优于其他2种模型;LSTM神经网络模型数据跟踪效果明显优于RNN模型

预测结果波动较小

模型稳定性和准确率较高。

Abstract

The NO_x produced in the combustion process of coal in thermal power plants is one of the most important sources of air pollutants. An effective prediction model should be established for reducing the NO_x emissions. In view of the massive and high dimensional system data of thermal power plants and the multivariable mutual coupling characteristics of the coal-fired boilers

the components analysis method was applied to extract the data from the DCS and eliminate the coupling of each feature variable. Then

the extracted data were used as input of the long short term memory(LSTM) neural network to establish the prediction model for NO_x emission. Moreover

this model

the conventional circulating neural network(RNN) model of NO_x emission prediction and the least square support vector machine(LSSVM) model were applied to the actual working condition data of an ultra supercritical 660 MW coal-fired boiler. The results show that

the prediction effect of the LSTM and RNN models was better than that of the LSSVM model. The prediction accuracy of the proposed model was 79% and the rootmean-square error was 0.398

indicating the proposed LSTM model is superior to the conventional RNN model and the LSSVM model. Furthermore

the data tracking effect of the model is obviously better than that of the RNN model

the prediction result fluctuates less and the model has higher stability and accuracy.

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

DOI：10.19666/j.rlfd.201805113

摘要

Abstract

关键词

Keywords

references

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