基于 KPCA-AGRU 神经网络的火电机组 NO x排放预测

针对火电机组锅炉燃烧过程中预测 NOx 排放过程存在的非线性和时序性特点,提出一种基于核主成分分析 (KPCA)和注意力机制(AM)的门控循环神经网络(GRU)氮氧化物预测模型。 首先选用 KPCA 对模型的输入变量 进行降维,消除冗余变量;其次,将筛选的变量数据作为 GRU 的输入,并采用网格搜索优化 GRU 的超参数;最后, 引入 AM 计算权值,实现区分输入特征功能,提高 NOx 预测模型精度。 通过某 330 MW 电站锅炉实际数据对 AGRU 预测模型仿真验证,并将 AGRU 模型、GRU 模型和 BP 神经网络模型的预测结果进行对比。 结果表明:基于 AGRU 的 NOx 预测模型的均方根误差和平均绝对误差较 BP 神经网络和 GRU 模型均有减少,可精准预测非线性时序燃 烧过程的 NO x 排放。

Prediction of NO x Emissions from Thermal Power Units Based on KPCA-AGRU Neural Network

Aiming at the nonlinear and sequential characteristics of NOx emission prediction in the boiler combustion process of thermal power units a NOx prediction model of gated recurrent unit GRU neural network based on kernel principal component analysis KPCA and attention mechanism AM was proposed. Firstly KPCA was selected to reduce the dimension of the input variables of the model and eliminate redundant variables. Secondly the filtered variable data was used as the input of GRU and the grid search was used to optimize the superparameters of GRU. Finally AM calculation weight was introduced to realize the function of distinguishing input characteristics and improve the accuracy of the NO x prediction model. The AGRU prediction model was simulated and verified by the actual data of a 330 MW power plant boiler and the prediction results of the AGRU model the GRU model and the BP neural network model were compared. The results show that the root mean square error and average absolute error of the NOx prediction model based on AGRU are less than those of the BP neural network and GRU model which can accurately predict the NOx emission in the nonlinear sequential combustion process.