Development of adaptive soft sensor based on statistical identification of key variables

An adaptive data-driven soft sensor is derived based on systematic dynamic key variables selection of a process system. The key variables are captured using statistical approaches. The on-line plant measurements can be directly selected as key features to estimate the tardily-detected quality variables. The statistical method adopted is the standard stepwise linear regression. The linear model is adapted as the on-line/off-line quality data becomes available. The adaptation of the model is implemented by standard Kalman filtering theory. The key variables are re-selected in case of new scenarios arrive and are detected by the soft senor. The real time data from an industrial O-xylene purification column is implemented to demonstrate the validity of the approach. Many different scenarios are simulated through an industrial standard dynamic simulator. The simulation results also showed the approach is adequate for the industrial applications.