Online Monitoring of Soft Sensor Performance: A Case Study of Cement Clinker Quality Parameters Soft Sensor

Abstract

The predictive ability of soft sensors deteriorates over time due to changes in the state of the plant and process characteristics. The results from the offline laboratory analyses of samples can be used to determine when a soft sensor requires recalibration; however, this approach is time-consuming. This paper presents a systematic approach in which a reverse model is developed for an online monitoring of the performance of soft sensor, the forward model. The proposed methodology is illustrated using a cement clinker quality parameters soft sensor as a case study. The reverse regression model gave rise to root mean squared error, coefficient of determination and worst case relative error values of 17.436, 0.9999 and 4.59%, respectively. Thus, it was concluded that, instead of the time-consuming approach of taking samples at the kiln exit for laboratory analysis, the developed reverse model can be used to provide plant operators with information about the predictive accuracy of the soft sensor.