【作者】 杜文莉； 官振强； 钱锋；

【Author】 DU Wen-li, GUAN Zhen-qiang, QIAN Feng (Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China)

【机构】 化工过程先进控制和优化技术教育部重点实验室华东理工大学；

【摘要】 针对目前静态软测量建模方法无法反映工业过程动态信息,造成预测模型精度低、鲁棒性差等问题,本文提出了一种基于最小二乘支持向量机(LS-SVM)和自回归-滑动平均模型(ARMA)的软测量建模方法。首先,建立了基于LS-SVM的软测量模型,利用ARMA模型对预测误差的动态估计,通过增加动态校正环节,实现了对静态模型的动态校正以改善系统动态响应特性。最后将上述方法用于乙烯精馏过程中乙烷浓度的软测量建模,仿真结果表明:与单一使用LSSVM模型相比,该方法具有跟踪性能好、泛化能力强等优点,是一种有效的软测量建模方法。更多还原

【Abstract】 Due to the static soft sensor modeling can not reflect the dynamic information of industrial process, which lead to worse estimation precision and robustness, a dynamic soft sensor modeling based on least square vector machine (LS-SVM) and ARMA time series predict modeling is presented. Establish a static soft sensor model based on LS-SVM firstly, and then make dynamic correct in static model use the dynamic estimation of predict error in ARMA to improve the dynamic response characteristics. Finally, the proposed LSSVM-ARMA is applied to predict the level of ethane in ethylene distillation. Simulation indicates that this method features good approximation and good generalization ability compared with LSSVM, and is of potential application in soft sensor.更多还原