大型燃煤电站锅炉在进行烟气脱硝时会产生较高的运行成本,建立有效的脱硝成本预测模型是对其进行经济性优化的基础.从某 660MW 火电机组的厂级监控信息系统(supervisory information system,SIS)选取历史运行数据,将BP神经网络算法与最小二乘支持向量机(least squares support vector machine,LSSVM)结合,利用BP网络算法对输入变量进行选择以降低模型的维数与复杂度,将筛选出来的变量作为 LSSVM 模型的输入,建立了脱硝成本预测的BP-LSSVM 模型.将该模型与单纯的 LSSVM 模型进行对比,结果表明通过神经网络变量选择,能有效降低模型的复杂度,提高模型的泛化能力,从而实现火电机组脱硝成本精确快速地预测.
Large coal-fired power plant boiler has high operating costs when using the flue gas denitration technology, and establishing an effective denitration cost model is the basis to implement economy optimization. On the basis of the operating data of a 660MW coal-fired boiler, back propagation(BP) algorithm and least squares support vector machine(LSSVM) were combined to predict the denitration cost. BP was applied to choose the input variables to reduce the dimension and complexity of the model, and the variables screened out was used as the final input of LSSVM to establish the BP-LSSVM model of denitration cost. The model was compared with the simple LSSVM model, and the result revealed that the model complexity is decreased and the model generalization ability is enhanced through the BP variable selection.