基于核极限学习机的SCR脱硝系统出口NOx浓度动态建模

Dynamic model for predicting nitrogen oxide concentration at outlet of selective catalytic reduction denitrification system based on kernel extreme learning machine

为解决变负荷工况下因模型输入变量较多、相关性大导致模型复杂度增加的问题,提出了一种将核极限学习机(KELM)和主成分分析(PCA)相结合的动态建模方法,并应用于选择性催化还原(SCR)脱硝系统出口处的氮氧化物(NO_(x))浓度预测.首先,将主成分分析应用于输入数据特征信息提取,并将提取信息的当前和过往序列值用作KELM模型的输入,以反映SCR出口处NO_(x)浓度的动态特征;然后,将SCR出口的NO_(x)浓度历史数据作为模型的输入,以提升模型精度;最后,利用优化算法确定模型最优参数.结果表明,与GPR、LSTM、CNN模型相比,所建动态模型的预测误差分别减少约78.4%、67.6%和59.3%,说明该模型结构可靠,能够准确预测SCR系统出口NO_(x)浓度.

To solve the increasing model complexity due to several input variables and large correlations under variable load conditions,a dynamic modeling method combining a kernel extreme learning machine(KELM)and principal component analysis(PCA)was proposed and applied to the prediction of nitrogen oxide(NO_(x))concentration at the outlet of a selective catalytic reduction(SCR)denitrification system.First,PCA is applied to the feature information extraction of input data,and the current and previous sequence values of the extracted information are used as the inputs of the KELM model to reflect the dynamic characteristics of the NO_(x)concentration at the SCR outlet.Then,the model takes the historical data of the NO_(x)concentration at the SCR outlet as the model input to improve its accuracy.Finally,an optimization algorithm is used to determine the optimal parameters of the model.Compared with the Gaussian process regression,long short-term memory,and convolutional neural network models,the prediction errors are reduced by approximately 78.4%,67.6%,and 59.3%,respectively.The results indicate that the proposed dynamic model structure is reliable and can accurately predict NO_(x)concentrations at the outlet of the SCR system.