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基于互信息变量选择的燃煤机组SCR脱硝系统PSO-ELM建模 被引量:1

PSO-ELM modeling of SCR denitrification system of coal-fired units based on mutual information variable selection
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摘要 针对燃煤机组SCR脱硝系统出口NOx浓度存在测量滞后以及吹扫时数据失真等问题,提出了一种基于特征提取和粒子群算法(PSO)优化极限学习机(ELM)超参数的燃煤机组SCR脱硝系统模型。利用互信息(MI)进行时间迟延补偿,采用最大相关最小冗余(mRMR)方法筛选辅助变量,通过PSO优化算法确定ELM最优超参数并建立预测模型,最后进行对比验证。仿真结果表明:采用本文方法所建立的PSO-ELM预测模型的均方误差和相关系数分别为0.9314 mg/m3和0.9786,预测精度高,能够为脱硝系统出口NOx的现场优化控制提供技术支持。 Aiming at the problems of NO x concentration at the outlet of selective catalytic reduction(SCR)denitration system of coal-fired units,such as measurement lag and data distortion during purging,a SCR denitration system model of coal-fired units based on feature extraction and particle swarm optimization(PSO)to optimize extreme learning machine(ELM)hyperparameters is proposed in this paper.Mutual information(MI)was used to compensate the time delay,maximum correlation minimum redundancy(mRMR)was used to screen the auxiliary variables,and the optimal ELM hyperparameters were determined by PSO optimization algorithm and the prediction model was established.Finally,the comparison and verification were carried out.The simulation results show that the mean square error and correlation coefficient of the PSO-ELM prediction model established by the method in this paper are 0.9314 mg/m 3 and 0.9786 respectively,with high prediction accuracy,which can provide technical support for the on-site optimization control of NO x at the exit of the denitrification system.
作者 张瑾 姜浩 金秀章 Zhang Jin;Jiang Hao;Jin Xiuzhang(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China)
出处 《网络安全与数据治理》 2023年第9期88-95,共8页 CYBER SECURITY AND DATA GOVERNANCE
关键词 互信息 粒子群算法 SCR脱硝系统 极限学习机 最大相关最小冗余 mutual information PSO algorithm SCR-DeNO x system extreme learning machine mRMR
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