基于双向深度学习的电站锅炉SCR脱硝系统入口NOx浓度预测

Pridiction of NOx Concentration at the Inlet of SCR Denitration System of Utility Boiler Based on Bidirectional Deep Learning

该文基于某660 MW电站锅炉的现场运行数据,在进行数据预处理的基础上,利用随机森林算法对输入变量进行特征提取以降低变量维数和消除变量间的相关性,并与双向长短时记忆神经网络(bi-directional long short-term memory,Bi-LSTM)相结合,建立了SCR脱硝系统入口NOx浓度的模型。将上述模型与其他建模方法进行比较,并将该模型实际应用于某电厂,作为精准喷氨控制的基础,结果表明通过Bi-LSTM建立的预测模型具有良好的预测能力,为进一步实施精准喷氨控制提供了模型基础。

This article based on the field operation data of a 660 MW utility boiler,on the basis of data preprocessing,the random forest algorithm is used to extract the features of input variables to reduce the dimension of variables and eliminate the correlation between variables.Combined with bi-directional long short-term memory(Bi-LSTM),the model of NOx concentration at the inlet of SCR denitration system is established.The above model is compared with other modeling methods and is applied to a power plant as the basis of precise ammonia injection control.The results show that the prediction model established by Bi-LSTM has good prediction ability,which provides a model basis for further implementation of accurate ammonia injection control.