基于记忆模式的NO_x支持向量回归预测研究

低NO_x排放是电站锅炉燃烧优化的主要目标之一,影响燃煤锅炉NO_x排放因素众多且复杂,对锅炉燃烧过程NO_x浓度进行准确预测是低NO_x燃烧优化的基础。机组全工况运行时表现出强时变性,静态预测模型难以保证预测精度,考虑到观测样本的时效性,模拟记忆模式对观测数据进行重采样,进而基于支持向量回归算法构建NO_x排放预测模型,构造一种基于记忆模式的支持向量回归算法。以某机组热态试验数据为基础,对算法进行了仿真分析,结果表明,该算法在保证回归建模精度的同时,在训练速度、稳定性以及泛化性能等方面较传统支持向量回归算法更有优势。

NOx Prediction Based on Memory Mode Support Vector Regression

Low NO_x emissions is one of the main objectives of the boiler combustion optimization,the impact factors of NO_x emissions in coal-fired boiler are numerous and complex.Accurate prediction of the NO_x concentration during the boiler combustion process is the key basis of low NO_x combustion optimization.The unit shows strong time varying characteristics at full working conditions,and the static prediction model is difficult to guarantee the prediction accuracy.The timeliness of the observation samples is taken into account.The observation data is resampled by imitating the memory mode,and then the NO_x emission prediction model is built using support vector regression(SVR) algorithm,so a memory model based on support vector regression algorithm is constructed.The algorithm is tested on the data sampled from a unit.The analysis results show that the proposed algorithm can not only ensure the regression modelling accuracy, but also has advantages in term of training speed,stability and generalization performance compared to the traditional support vector regression algorithm.