Selective Use of Adaptive Soft Sensors Based on Process State

Soft sensors are widely used to realize efficient operations in chemical processes because some governing variables, such as product quality, cannot be measured directly through hardware in real time. One of the design problems of soft sensors is the degradation of their prediction accuracy. To reduce degradation, a range of adaptive models has been developed, such as moving window, just-in-time, and time difference models. However, none of these adaptive models performs well in all process states. To address this problem, we developed an online monitoring system using multivariate statistical process control to select the appropriate adaptive model for each process state. The proposed method was applied to dynamic simulation data and empirical industrial data. Higher predictive accuracy than from traditional adaptive models was achieved. This novel approach may be used to reduce the maintenance cost of soft sensors.

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