Semi-Supervised Hybrid Local Kernel Regression for Soft Sensor Modelling of Rubber-Mixing Process
Joint Authors
Li, Ping
Shao, Fenjing
Yu, Haiqing
Ji, Jun
Wu, Shunyao
Sui, Yi
Li, Shujing
He, Fengjiao
Liu, Jinming
Source
Advances in Polymer Technology
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-31
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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Meanwhile, traditional global learning algorithms suffer from the outdated samples while online learning algorithms lack practicality since too many labelled samples of current batch are required to build the soft sensor.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.
American Psychological Association (APA)
Yu, Haiqing& Ji, Jun& Li, Ping& Shao, Fenjing& Wu, Shunyao& Sui, Yi…[et al.]. 2020. Semi-Supervised Hybrid Local Kernel Regression for Soft Sensor Modelling of Rubber-Mixing Process. Advances in Polymer Technology،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1130336
Modern Language Association (MLA)
Yu, Haiqing…[et al.]. Semi-Supervised Hybrid Local Kernel Regression for Soft Sensor Modelling of Rubber-Mixing Process. Advances in Polymer Technology No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1130336
American Medical Association (AMA)
Yu, Haiqing& Ji, Jun& Li, Ping& Shao, Fenjing& Wu, Shunyao& Sui, Yi…[et al.]. Semi-Supervised Hybrid Local Kernel Regression for Soft Sensor Modelling of Rubber-Mixing Process. Advances in Polymer Technology. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1130336
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130336