Detecting stationary gain changes in large process systems

Abstract

Stationary process gains are critical model parameters to determine targets in commercial MPC technologies. Consequently, important savings can be reached by acceding to an early prevention method capable of detecting whether the actual process moves away from the modeled dynamics or not, particularly by indicating when the process gains are not more represented by those included in the model identified during commissioning stages. In this first approach, a subspace identification method is used under open loop process condition to develop a process gain-matrix estimator. The main reason for using the subspace identification method is that it works directly with raw data and that the development is intended for monitoring future applications under multivariable closed-loop optimizing control where the transient regime is a frequent scenario. The objective of this paper is to present a method capable of detecting those gains of a multivariable model that start moving away from the original values. The anticipated knowledge of these events could provide a warning to process engineers and prevent from targeting process conditions with wrong gain estimations. The regular follow-up of the gain matrix should also help to localize those dynamics needing an updating identification.