Adaptive Soft Sensor Based on a Moving Window Just-in-time Learning LS-SVM for Distillation Processes

In order to measure the distillation processes compositions under the time-varying conditions, an adaptive soft sensor model is proposed in this paper for composition quality prediction. In the traditional approach, the least squares-support vector machine regression (LS-SVM) method is used to build the composition quality prediction model. In order to implement on-line soft sensing of distillation compositions, the just-in-time learning strategy is used to update the dynamic model. But as more and more historical data is stored in the database, the just-in-time learning updating strategy will be very time consuming. To improve the calculation efficiency, the moving window just-in-time learning least squares-support vector machine regression (MWJIT-LS-SVM) by using a moving window with selective moving window size algorithm is proposed in this paper. The simulation results show that the proposed method achieved higher predictive accuracy than traditional methods when time-varying changes in chemical distillation processes.