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基于BP神经网络和SVM的电厂粉尘浓度在线监测Online Monitoring of Dust Concentration in Power Plant Based on BP Neural Network and SVM
粉尘浓度, 在线监测, BP神经网络, 支持向量机Dust Concentration, On-Line Monitoring, BP Neural Network, Support Vector Machine (SVM)
《Advances in Energy and Power Engineering》, Vol.4 No.4, 2016-08-17
基于电厂经济环保运行的要求，需对电厂的污染物排放浓度实时监测。本文以排烟粉尘浓度为例，通过分析电厂DCS系统的在线监测数据，建立了BP神经网络和支持向量机两种粉尘浓度的在线监测模型。对模型进行实例验证(数据来源于某电厂600 MW机组)，结果显示BP模型的预测精度达到96%以上，而SVM模型精度则达到97.5%以上。从总体上看，这两种模型对于粉尘浓度在线监测效果都比较理想，相对而言SVM模型的模拟的精度较高，且具有更高的泛化能力。For the purpose of achieving online monitoring of dust concentration, the online monitoring pa-rameters in DCS system are adopted to analyze the factors which influence the concentration of smoke dust, and the BP neural network and support vector machine are used to propose an on-line monitoring method for dust concentration proposed. Simulation and prediction are based on the operating data of a power plant 600 MW unit. The simulation results show that the prediction accuracy of the two models is both more than 96%, and the prediction error of BP model is less than 4%, while the error of SVM model is even less than 2.5%. On the whole, these two models are ideal for dust concentration monitoring, but the accuracy of the SVM model is relatively higher, and it has higher generalization ability, and is more stable. Therefore, it can be a kind of effective method for on-line monitoring.