No abstract

No abstract

Soft sensors based on dynamic PLS (DPLS) have been widely used in industrial applications for predicting hard-to-measure quality variables. However, DPLS is prone to overfitting due to an increasing number of model inputs. A plethora of approaches have been proposed to improve DPLS-based soft sensors, among which variable selection has been a prevailing one. Recently, a new method termed as DPLS-TS has been proposed to penalize dynamic parameters in DPLS using a temporal smoothness regularization, which helps reduce model complexity and deliver smooth predictions for quality variables. In this work we present a comparative study of temporal smoothness regularization and variable selection in terms of their improvements in prediction performance when a large number of lagged time series data are involved. Comparisons are performed through a simulated case of crude distillation unit.