基于改进核极限学习机和集成算法的脱硫出口SO_(2)浓度预测  

Prediction of SO_(2) Concentration at Desulfurization Outlet Based on Improved Kernel Extreme Learning Machine and Integrated Algorithm

作　　者：闫浩思 赵文杰[1] YAN Haosi;ZHAO Wenjie(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China)

机构地区：[1]华北电力大学控制与计算机工程学院,河北保定071003

出　　处：《华北电力大学学报（自然科学版）》2024年第5期108-117,共10页Journal of North China Electric Power University：Natural Science Edition

摘　　要：脱硫出口SO_(2)浓度的准确预测对实现脱硫系统经济运行具有重要意义,针对脱硫出口SO_(2)浓度影响因素众多,难以准确预测这一问题,提出了基于龙格库塔优化的核极限学习机(KELM)和改进AdaBoost集成算法相结合的预测模型。首先采用核极限学习机作为弱预测器,利用AdaBoost集成算法组合构建强预测器,通过调整脱硫系统不同工况下运行数据权重,建立了一种基于AdaBoost集成算法的出口SO_(2)浓度预测模型。为进一步提升模型学习性能和预测精度,通过引入惩罚系数和先验知识参数改进AdaBoost算法的损失函数,运用龙格库塔算法对KELM的正则系数C和核参数S进行寻优,克服初始参数设置对模型稳定性和预测精度的影响。最后利用电厂运行数据进行仿真实验,结果表明,所建立的出口SO_(2)浓度集成模型预测性能优越、准确度高,能够为脱硫系统优化控制提供技术支持。The accurate prediction of the SO_(2) concentration at the desulfurization outlet is of great significance to realize the economic operation of the desulfurization system.Aiming at the problem that SO_(2) concentration at desulfurization outlet is difficult to predict accurately due to many influencing factors,we proposed a prediction model based on Runge Kutta optimization kernel limit learning machine(KELM)and improved AdaBoost integration algorithm.Firstly,the kernel extreme learning machine was used as the weak predictor,and the strong predictor was constructed by using the AdaBoost ensemble algorithm.By adjusting the operating data weight of the desulfurization system under different working conditions,we established a prediction model of outlet SO_(2) concentration based on the AdaBoost ensemble algorithm.In order to further improve the learning performance and prediction accuracy of the model,the loss function of the AdaBoost algorithm was improved by introducing penalty coefficients and prior knowledge parameters,and the Runge-Kutta algorithm was used to optimize the regularity coefficient C and kernel parameter S of KELM to overcome the influence of the initial parameter setting on the model stability and prediction accuracy.Finally,the simulation experiment was carried out by using the power plant operation data and the results show that the established integrated model of outlet SO_(2) concentration has higher prediction performance and accuracy,and can provide technical support for the on-site optimal control of the desulfurization system.

关 键 词：核极限学习机 AdaBoost集成学习 龙格库塔算法 脱硫出口SO_(2)浓度 预测模型 

分 类 号：X773[环境科学与工程—环境工程]