基于多向核偏最小二乘的间歇过程在线监控（英文）

Batch process on-line monitoring based on multiway kernel partial least squares

提出了一种基于多向核偏最小二乘的间歇过程在线监控方法。传统的间歇过程监控方法、如多向偏最小二乘方法,实际上是一种线性监控方法,因此不适合于非线性间歇过程监控。为此,提出了核偏最小二乘方法,由于该方法能获取变量间的非线性关系;另外,它只是求解代数运算,并不涉及到复杂的非线性优化问题,所以,这里将核偏最小二乘扩展到间歇过程在线监控、即提出多向核偏最小二乘的间歇过程在线监控方法。将该方法应用于青霉素补料分批发酵过程仿真监控,与传统的多向偏最小二乘方法相比,结果表明其具有更好的监控性能。

An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares （MKPLS） was presented. It is known that conventional batch process monitoring methods, such as multiway partial least squares （MPLS）, are not suitable due to their intrinsic linearity when the variations are nonlinear. To address this issue, kernel partial least squares （KPLS） was used to capture the nonlinear relationship between the latent structures and predictive variables. In addition, KPLS requires only linear algebra and does not involve any nonlinear optimization. In this paper, the application of KPLS was extended to on-line monitoring of batch processes. The proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation process. And the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring.