改进PSO优化参数的LSSVM燃煤锅炉Nox排放预测

为了提高燃煤锅炉NOX排放浓度预测的准确度,更好地进行氮氧化物的污染监测,提出了一种结合最小二乘支持向量机(Least squares support vector machines,LSSVM)和改进的粒子群优化算法(Particle swarm optimization,PSO)的预测方法。依据LSSVM预测原理及其参数选择的不确定性,采用一种改进的PSO优化算法对模型参数进行寻优,建立锅炉燃烧NOX排放特性模型,并与另两种方法结果进行比较。结果表明:LSSVM是一种有效的建模方法,有较高的拟合度;改进的PSO与LSSVM结合可改善模型的预测精度和泛化能力,在NOX排放浓度预测方面明显优于其他两种参数优化算法,对NOX排放预测有指导意义。

NOx Emission Concentration of Coal-Fired Boiler Prediction Based on Improved PSO Parameter Optimized LSSVM

In order to improve the accuracy of NOx emission concentration prediction of the coal-fired boiler and more accurately monitor the NOx pollution, this paper proposes a prediction method based on the least squares support vector machines (LSSVM) and the improved particle swarm optimization (PSO). According to LSSVM forecasting theory as well as the uncertainty of LSSVM parameter selection, an improved PSO algorithm to optimize the parameters of the model is used, a model of NOx emission characteristics is established, and the prediction results are compared with the results of other two methods simultaneously. Results indicate th at LSSVM is an effective modeling method which has higher fitting degree; the combination of improved PSO and LSSVM can improve the prediction accuracy and the generalization ability, and LSSVM is superior to the other two parameter optimization algorithms in the NOx emissions concentration forecast.