基于FAR-HK-ELM的燃煤电站锅炉NO_(x)排放预测

NO_(x) Emission Prediction of Coal Fired Utility Boiler Based on FAR-HK-ELM

结合快速属性约简(FAR)与混合核极限学习机(HK-ELM)算法,提出了一种基于FAR-HK-ELM的燃煤电站锅炉NO_(x)排放预测方法。该方法首先通过FAR算法筛选出影响NO_(x)排放量的主要影响属性,剔除高维特征的冗余信息;然后构建基于全局多项式核函数(Poly)和局部高斯径向基核函数(RBF)的HK-ELM对NO_(x)排放进行建模。通过带约束的权重线性递减粒子群寻优算法和交叉验证来获得模型的最优参数。以某燃煤电站锅炉运行系统为例,将模型应用于真实运行数据并进行预测分析验证。实验结果表明,与BP、SVM、PK-ELM、GK-ELM和HK-ELM等模型相比,所提方法进一步提高了模型的泛化能力。该研究为燃煤电站锅炉系统的燃烧优化奠定了基础。

A prediction method for NO_(x) emission of coal-fired utility boiler based on FAR-HK-ELM was proposed by combining the Fast Attribute Reduction(FAR)and Hybrid Kernel Extreme Learning Machine(HK-ELM)algorithms.First,the main influencing attributes of NO_(x) emission are selected by FAR algorithm,and the redundant information of high-dimensional characteristics is eliminated;Then,HK-ELM based on global polynomial kernel and local gaussian radial basis function is constructed to model NO_(x) emission,and the optimal parameters of the model are obtained through the constrained weight linear decreasing particle swarm optimization algorithm and cross validation.By taking a coal-fired utility boiler operation system as an example,the model was applied to the real operation data for prediction analysis and verification.Compared with BP,SVM,PK-ELM,GK-ELM and HK-ELM models,the proposed method further improves the generalization ability of the model.This study lays a foundation for the combustion optimization of coal-fired utility boiler system.