Performance-Based Plant-Model-Mismatch Detection in Soft-Sensor Control Loops

Abstract

The predictive performance of soft sensors deteriorates over time which is called the performance change of a soft sensor. These changes occur due to differences between the current characteristics of the process or plant and the soft sensor model. The deviation is a type of plant-model mismatch (PMM). Initially, this mismatch may be acceptable. However, over time, the PMM can become so large that it affects the prediction quality of the soft sensor and may become unacceptable. This paper develops a new method to evaluate the impact of PMM on closed-loops with soft sensors. Using coprime factorisation and small-gain theory, a performance-change index is developed to characterise the PMM-induced performance degradation. Then, a performance-based online PMM detection method is proposed using this performance-change index. To validate the effectiveness of the proposed algorithm, we use a numerical example and a continuous stirred tank reactor (CSTR). It is shown that that the proposed index can detect the change of the PMM.