中文摘要：

为解决工业过程软测量中的变量维数高、数据相互耦合、非线性强等问题,提出了基于主曲线的软测量方法。其中的基于主曲线的非线性回归模型借鉴了PLS的基本思想,采用主曲线提取隐变量信息的同时考虑了自变量与因变量的相关性;在隐变量空间中,采用多项式函数拟合隐变量之间的非线性关系。在实例研究中,分别采用纯函数数据和氯乙烯精馏塔实时运行数据对该模型进行了验证。仿真结果表明,该模型所需要的隐变量数目比传统的PLS模型更少,并且能够实现更为精确的预测,可较好地处理工业过程中存在的数据高耦合度以及强非线性问题。

英文摘要：

In order to solve the problems of high dimension,data coupling with each other and nonlinearity in soft sensing of industrial processes,a new soft sensing method based on principal curves was proposed.The nonlinear regression model based on principal curves borrowed the basic idea of PLS and extracted latent variables by principal curves,and the correlation between dependent variables and independent variables was considered simultaneously.A polynomial function was used to fit nonlinear relations between latent variables in latent space.Data from nonlinear functions and VCM（vinyl chloride monomer）distillation columns respectively were used to validate the model.The results showed that the model could solve the nonlinearity problems which included high data coupling in the industrial process with less latent variables and more accurate predictions than traditional PLS model.