摘要
为了解决电站锅炉操作人员依赖经验调节锅炉运行参数降低SCR入口NOx浓度,提高脱硝效果的问题,提出一种SCR入口处NOx浓度预测方法。该方法建立了基于卷积神经网络和长短期记忆神经网络的CNN(1D)-LSTM模型,通过提取锅炉在时序上的特征参数,可预测5 min后SCR入口处NOx浓度。电厂运行人员可将该模型的预测结果作为SCR入口处NOx浓度的重要参考,更加有效地调节锅炉参数进行脱硝优化。结果表明,预测3 min后SCR入口处NOx浓度LSTM模型优于CNN(1D)-LSTM;预测5 min后的SCR入口浓度CNN(1D)-LSTM模型相比于LSTM模型预测精度有很大的所提高,在测试集上E_(mape)为7.05%,取得了期望的效果。
In order to solve the problem that power plant boiler operators rely on experience to adjust boiler operating parameters to reduce the NOx concentration at the SCR inlet and improve the denitration performance,a method for predicting the NOx concentration at the SCR inlet is proposed.This method establishes a CNN(1D)-LSTM model based on convolutional neural network and long short-term memory neural network.The NOx concentration at the SCR inlet can be predicted after 5 min.Power plant operators can use the prediction results of the model as an important reference for the NOx concentration at the SCR inlet,and more effectively adjust boiler parameters for denitrification optimization.The results show that the LSTM model for predicting the NOx concentration at the SCR inlet after 3 min is better than CNN(1D)-LSTM;the CNN(1D)-LSTM model for predicting the SCR inlet concentration after 5 min has a great prediction accuracy compared with the LSTM model.The E_(mape) on the test set is 7.05%.The desired effect was achieved.
作者
刘建军
赵旭
张卫东
马达夫
Liu Jianjun;Zhao Xu;Zhang Weidong;Ma Dafu(School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;Shanghai Power Equipment Research Institute Co.,Ltd,Shanghai 200240,China)
出处
《电子测量技术》
北大核心
2023年第13期59-65,共7页
Electronic Measurement Technology
