SCR脱硝系统NOx排放浓度建模与仿真

由于选择性催化还原(Selective catalytic reduction, SCR)脱硝系统在工况变化时具有非线性、大滞后和强干扰性的特点,提出基于互信息(Mutual information, MI)和核隐变量正交投影(Kernel-based Orthogonal Projections to Latent Structures, KOPLS)对NO_x排放浓度建立模型。利用互信息估计输入变量时延,并实现样本相空间重构;利用KOPLS建模。对标准数据集仿真,KOPLS具有较强的泛化、非线性逼近和抗噪能力。现场数据分析,MI-KOPLS与KOPLS相比,在训练和测试时RMSE减小17%和22%,使预测更精确;MI-KOPLS与其它算法相比,测试时RMSE和MAPE达到最小值3.1886mg/m³和13.5917%,说明预测值最接近真实值,验证了其有效性。

Modelling and Simulation for NOx Emission Concentration of SCR Denitrification System

The selective catalytic reduction (SCR) denitrification system has the features of non-linearity, large lag and strong disturbance, when the operating condition changes. Based on mutual information (MI) and Kernel-based Orthogonal Projections to Latent Structures (KOPLS), the model for NO_x emission concentration is proposed. The time-delay of each input variable is estimated by mutual information, and phase space construction is performed, KOPLS is utilized to modelling. KOPLS shows the merits of strong generalization, nonlinear fitting and anti-noise in the simulation of benchmark datasets. According to field data analysis, RMSE of MI-KOPLS in training and test are reduced by 17% and 22% respectively. Compared with KOPLS, MI-KOPLS predicts more accurately. Compared with other algorithms, RMSE and MAPE of MI-KOPLS reach minimum values 3.1886 mg/m³ and 13.5917% in test respectively, what indicates that the predicted value is the closest to real value, and the effectiveness of MI-KOPLS is verified.