核函数方法在丙烯腈收率软测量建模中的应用

Application of Kernel-Based Methods to Soft Sensor Modeling of Selectivity to Acrylonitrile

介绍了核函数方法的基本原理及两种核函数统计建模方法;提出了用核函数PLS与核函数PCR建立工业丙烯腈生产过程丙烯腈收率软测量模型,以便更有效地处理过程非线性、多输入和数据共线性等复杂特性。对比研究发现,基于核函数方法的软测量模型要优于线性统计模型,而核函数PLS模型性能优于核函数PCR。

Principles of kernel-based methods and two kernel-based statistical modeling techniques are introduced. Soft sensor modeling of selectivity to acrylonitrile using kernel PLS and kernel PCR is proposed to cope with the nonlinearity and multi high dimension of input and collinearity problem of process. It is found that the performance of kernel-based methods is superior to linear statistical model and that of kernel PLS is also superior to kernel PCR.