A Comparative Study on Improved DPLS Soft Sensor Models Applied to a Crude Distillation Unit∗. Issue 8 (2015)

• Record Type: Journal Article
• Authors: Shang, Chao; Gao, Xinqing; Yang, Fan; Lyu, Wenxiang; Huang, Dexian
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 8(2015)
• Page Start: 234
• Page End: 239
• Publication Date: 2015
• Subjects: Partial least squares -- quality prediction -- variable selection -- temporal smoothness regularization -- soft sensor; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.08.187
• Languages: English
• ISSNs: 2405-8963

Abstract

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 over-fitting 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.