基于自适应递推最小二乘支持向量机的磨煤机一次风量软测量模型

Soft measurement model of primary air flow of coal mill based on self-adaptive recursive LSSVM

针对离线最小二乘支持向量机(LSSVM)以及无稀疏策略的在线LSSVM在过程建模工程应用的局限性,提出了一种基于选择性递推以及自适应更新模型参数的LSSVM软测量模型。该方法将快速留一交叉验证(FLOO-CV)误差作为模型更新阈值,前向学习时,根据更新阈值只引入预报误差较大的样本更新模型,提高了模型的稀疏性;后向样本修剪时,仅删除FLOO-CV误差最小的样本,提高了模型的全局推广能力。应用电厂实际运行数据验证该模型并对磨煤机一次风量进行在线预测,并研发了一套在线软测量平台。将该平台在某1000 MW机组进行现场验证,结果表明,该平台对一次风量有较高的预测精度,可以在流量传感器出现故障时代替其工作,保证磨煤机一次风量信号的稳定性和可靠性。

Due to the limitations of process modeling engineering applications of off-line least square support vector machine(LSSVM)and no-sparse strategy online LSSVM,an online recursive LSSVM soft sensor model based on selective recursive and self-adaptive updating model parameters is presented.This method uses the fast leave-oneout cross-validation(FLOO-CV)error as the modle update threshold.In forward learning,the FLOO-CV prediction error-based threshold is used to enhance the sparse ability of the model by only introducing the samples with larger predictive error and the FLOO-CV is also utilized in backward learning only to delete redundant samples with the smallest error,which enhances the model’s generalization ability.In order to verify the model by using the actual operation data of the power plant and forecast the primary air flow of the coal mill online,a set of online soft sensor platform is developed.The platform is verified on the field of a 1000 MW unit,and the results show that,this platform has high prediction accuracy for primary air flow,it can replace the flow meter with failure and ensure the stability and reliability of primary air flow signal of the coal mill.