Interpretable, Online Soft-Sensors for Process Control

When building a soft sensor for control purposes, it is essential that information regarding the dependence of the soft sensor on the input variables can be extracted from the underlying model. We present an online, adaptive soft sensor with the capability of providing online feedback regarding the dependence of the soft sensor on input variables through an online contribution plot. Two core methods (recursive PLS and adaptive decision trees) producing highly interpretable models are used within a modification of a previously established soft-sensor framework. This framework is used to build a soft sensor on real-world industrial data.