Performance Change Recovery in Soft-Sensor Control Loops

In most industrial settings, soft sensors are used to measure variables that are challenging to estimate. However, due to various factors, the estimated performance may vary over time, causing it to fail in estimating key variables quickly and accurately enough, which could result in financial losses and security risks. The variation in the predictive performance of a soft sensor is referred to as the performance drift of the soft sensor. These changes occur due to the differences between the current characteristics of the process or plant and the soft sensor. This discrepancy is also defined as plant-model mismatch (PMM). Therefore, once the soft sensor is designed, a way to recover the performance change is required. This paper proposes a method that reduces the impact of PMM without updating the soft sensor itself. This method reconstructs the closed-loop controller based on the performance change index (PCI) for the soft sensor. Then, the online identification of the fault model is studied. Finally, the modification rules for the controller are given using the Youla-Kučera parameterisation. This approach is tested on a three-tank system, where it is shown that the performance changes of the soft sensor caused by PMM are recovered.