Semi-Supervised Hybrid Local Kernel Regression for Soft Sensor Modelling of Rubber-Mixing Process. (31st January 2020)

• Record Type: Journal Article
• Authors: Yu, Haiqing; Ji, Jun; Li, Ping; Shao, Fengjing; Wu, Shunyao; Sui, Yi; Li, Shujing; He, Fengjiao; Liu, Jinming
• Other Names: Yao Yuan Guest Editor.
• Is Part Of: Advances in polymer technology. Volume 2020(2020)
• Publication Date: 2020-01-31
• Subjects: Plastics -- Periodicals; Polymers -- Periodicals; 668.9
• DOI: 10.1155/2020/6981302
• Languages: English
• ISSNs: 0730-6679

Abstract

Abstract : Soft sensor techniques have been widely adopted in chemical industry to estimate important indices that cannot be online measured by hardware sensors. Unfortunately, due to the instinct time-variation, the small-sample condition and the uncertainty caused by the drifting of raw materials, it is exceedingly difficult to model the fed-batch processes, for instance, rubber internal mixing processing. Meanwhile, traditional global learning algorithms suffer from the outdated samples while online learning algorithms lack practicality since too many labelled samples of current batch are required to build the soft sensor. In this paper, semi-supervised hybrid local kernel regression (SHLKR) is presented to leverage both historical and online samples to semi-supervised model the soft sensor using proposed time-windows series. Moreover, the recursive formulas are deduced to improve its adaptability and feasibility. Additionally, the rubber Mooney soft sensor of internal mixing processing is implemented using real onsite data to validate proposed method. Compared with classical algorithms, the performance of SHLKR is evaluated and the contribution of unlabelled samples is discussed.