燃煤电厂NOx排放量的叠加综合预测,Fuel

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燃煤电厂NOx排放量的叠加综合预测
Fuel ( IF 6.7 ) Pub Date : 2021-04-01 , DOI: 10.1016/j.fuel.2020.119748
Zhaowei Yuan , Lei Meng , Xiaobing Gu , Yuyong Bai , Huanmin Cui , Chengyu Jiang

摘要准确测量脱硝反应器入口处的氮氧化物（NOx）浓度对控制燃煤电厂NOx排放具有重要意义。因此，探索了一种基于叠加广义集成方法（SGEM）的NOx排放预测方法。首先，采用主成分分析（PCA）方法消除原始辅助变量之间的相关性。随后，采用互信息(MI)方法选择对入口NOx排放影响较大的辅助变量。最后，在选定的辅助变量下，基于SGEM建立了NOx排放模型。在SGEM方法中，反向传播神经网络（BPNN），支持向量回归（SVR）和决策树（DT）被用作基础模型，线性回归（LR）被用作元模型。每个基础模型的最佳超参数由网格搜索和 10 倍交叉验证方法确定。基于分布式控制系统（DCS）变工况历史数据对比结果表明，SGEM的预测和实测结果集中在45度方向，相对误差（RE）主要分布在−5 mg/Nm3 和 5 mg/Nm3。同时，SGEM 实现了最高的相关系数平方 (R2) 和最小的均方根误差 (RMSE)、平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE)，而不是任何单独的方法。

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Prediction of NOx emissions for coal-fired power plants with stacked-generalization ensemble method

Abstract Measuring the nitrogen oxides (NOx) concentration accurately at the inlet of the denitration reactor plays an important role in controlling the NOx emissions for coal-fired power plants. Therefore, a NOx emissions prediction method based on stacked-generalization ensemble method (SGEM) was explored. Firstly, principal component analysis (PCA) method was used to eliminate the correlations between the original auxiliary variables. Subsequently, the mutual information (MI) method was used to select the auxiliary variables that have a greater impact on the inlet NOx emissions. Finally, The NOx emissions model was established based on SGEM with the selected auxiliary variables. Among the SGEM method, the back-propagation neural network (BPNN), support vector regression (SVR) and decision tree (DT) were used as the base models and the linear regression (LR) as the meta-model. The optimal hyper-parameters of each base model were determined by the grid search and 10-folds cross-validation methods. Based on the historical data with variable conditions from distributed control system (DCS), the comparison results showed that the predicted and measured results of SGEM were concentrated in the direction of the 45-degree, and the relative error (RE) was mainly distributed between −5 mg/Nm3 and 5 mg/Nm3. Simultaneously, the SGEM achieved the highest square of correlation coefficient (R2) and the smallest root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) than any individual method. The 10-folds cross-validation results also showed that the SGEM method had strong robustness and generalization ability.

更新日期：2021-04-01

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