A localized adaptive soft sensor for dynamic system modeling

• Published in: Chemical engineering science Vol. 111; pp. 350 - 363
• Main Authors: Ni, Wangdong, Brown, Steven D., Man, Ruilin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 24.05.2014
• Subjects: Adaptive thresholding; Algorithms; Averaged bias updating; Averaged LASS; Chemical engineering; Dynamical systems; Dynamics; LASS; Mathematical models; Moving-window; Sensors; Strategy; Thresholds; Moving-window; Averaged bias updating; Averaged LASS; LASS; Adaptive thresholding
• ISSN: 0009-2509; 1873-4405
• DOI: 10.1016/j.ces.2014.03.002

A new, localized adaptive soft sensor (LASS) is presented in this paper. The algorithm addresses two issues that arise in modeling by localized adaptive RPLS (LARPLS) with a forgetting factor: (1) the soft sensors used in LARPLS and (2) the preset threshold for updating the local region of the process (re-localization). Instead of using recursive partial least squares (RPLS) with a forgetting factor in the LASS model, an RPLS algorithm is used in a moving window to provide the local learning framework for online prediction in the soft sensor. This approach enables better predictive performance from a simpler model. Adaptive thresholding for re-localization is used in the new LASS model in place of the preset threshold in LARPLS to track process dynamics more effectively. A new, averaged bias updating strategy is applied to the LASS models, to avoid the need to preselect a weighting factor. Averaging of this newly developed LASS model and a model based on LARPLS with adaptive thresholding is also examined. A comparison is made with several other global and local models, and the newly developed LASS with a moving-window is shown to be more adaptive in the face of process changes, as demonstrated in the modeling of three different chemical processes. •We developed a LASS model for dynamic process modeling.•LASS can effectively track the process dynamics with high adaptability.•LASS employs a moving window and adaptive thresholding to model process dynamics.•Two averaged strategies were developed to improve the LASS model׳s performance.•Two dynamic, chemical processes are used to present the performance of LASS model.