基于集合经验模态分解和长短期记忆网络的催化裂化装置氮氧化物排放预测

Prediction of NOx emission from fluid catalytic cracking unit based on ensemble empirical mode decomposition and long short-term memory network

氮氧化物(NOx)是催化裂化(FCC)装置再生烟气中的主要污染物之一,准确预测NOx的排放浓度可有效避免炼化企业污染事件的发生。鉴于污染物排放数据具有非平稳、非线性和长记忆等特性,为了提高污染物排放浓度预测精度,提出一种基于集合经验模态分解(EEMD)和长短期记忆网络(LSTM)的耦合模型。将NOx排放浓度数据经过EEMD为若干个固有模态函数(IMF)和一个残差序列;根据IMF子序列与原始数据之间的相关性分析,剔除极弱相关的信号分量,有效减小原信号数据中的噪声;将IMF序列集分为高、低频两部分,分别进入不同深度的LSTM网络;最终,将子序列的预测结果反变换得到NOx排放浓度。实验表明,在催化裂化装置NOx排放预测中,对比LSTM的表现,EEMD-LSTM耦合模型在均方误差(MSE)、平均绝对误差(MAE)分别减小了46.7%、45.9%;在决定系数R;上增大了43%,实现了更高的预测精度。

Nitrogen oxide(NOx)is one of the main pollutants in the regenerated flue gas of Fluid Catalytic Cracking(FCC) unit.Accurate prediction of NOx emission can effectively avoid the occurrence of pollution events in refinery enterprises.Because of the non-stationarity,nonlinearity and long-memory characteristics of pollutant emission data,a new hybrid model incorporating Ensemble Empirical Mode Decomposition(EEMD) and Long Short-Term Memory network(LSTM) was proposed to improve the prediction accuracy of pollutant emission concentration.The NOx emission concentration data was first decomposed into several Intrinsic Mode Functions(IMFs)and a residual by using the EEMD model.According to the correlation analysis between the IMF sub-sequences and the original data,the IMF sub-sequences with low correlation were eliminated,which could effectively reduce the noise in the original data.The IMFs could be divided into high and low frequency sequences,which were respectively trained in the LSTM networks with different depths.The final NOx concentration prediction results were reconstructed by the predicted results of each sub-sequences.Compared with the performance of LSTM in the NOx emission prediction of FCC unit,the Mean Square Error(MSE),Mean Absolute Error(MAE)were reduced by 46.7%,45.9%,and determination coefficient(R;)of EEMD-LSTM was improved by 43% respectively,which means the proposed model achieves higher prediction accuracy.