【摘要】：SCR系統(tǒng)脫硝過程普遍應用的建模方法分為機理建模和數(shù)據(jù)建模兩種,但這兩種方法之間的對比研究相對較少。確定兩種方法各自的適用范圍,從而選取適宜的建模方法是確保模型準確性的前提。文中根據(jù)E-R反應機理以及支持向量機(support vector machine,SVM)、BP神經(jīng)網(wǎng)絡(luò)(BP neural network,BPNN)、核偏最小二乘(kernel partial least squares,KPLS)等方法分別建立了SCR系統(tǒng)的機理和數(shù)據(jù)動態(tài)模型。采用現(xiàn)場實際運行數(shù)據(jù)對模型進行驗證,并根據(jù)模型精度評價指標以及建模耗時對各種建模方法進行對比。結(jié)果表明,在局部工況樣本條件下,機理模型計算精度更高,計算量較小。在全局工況樣本條件下,數(shù)據(jù)模型的擬合和泛化能力更強,但是數(shù)據(jù)模型的計算量更大,建模耗時更長。
[Abstract]:The modeling methods widely used in the denitrification process of SCR system can be divided into two types: mechanism modeling and data modeling, but the comparative study between the two methods is relatively few. In order to ensure the accuracy of the model, it is necessary to determine the applicable scope of the two methods, and then select the appropriate modeling method to ensure the accuracy of the model. In this paper, the mechanism and data dynamic models of SCR system are established according to Eur reaction mechanism, support vector machine (support vector machine,SVM), BP) neural network (BP neural network,BPNN), kernel partial least squares (kernel partial least squares,KPLS) and so on. The model is verified by field running data, and various modeling methods are compared according to the evaluation index of model precision and the time consuming of modeling. The results show that the calculation accuracy of the mechanism model is higher and the calculation amount is less under the local working conditions. The fitting and generalization ability of the data model is stronger under the condition of the global working condition sample, but the calculation of the data model is larger and the modeling time is longer.
【作者單位】： 新能源電力系統(tǒng)國家重點實驗室(華北電力大學);海南電力技術(shù)研究院;
【基金】：中央高校基本科研業(yè)務(wù)費專項資金資助(2016MS47,2015XS69) 中國南方電網(wǎng)有限責任公司科技項目(073000KK51140001)~~
【分類號】：X773

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