LAPSE:2020.0388
Published Article
LAPSE:2020.0388
Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data
April 14, 2020
Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge while the BB models are developed using data collected from the process. The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators. In this work, various design aspects of the GB models are discussed followed by their application in the process industry. In addition, the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated.
Record ID
Keywords
big data analytics, internet of things, Machine Learning, sensor 4.0
Subject
Suggested Citation
Ahmad I, Ayub A, Kano M, Cheema II. Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data. (2020). LAPSE:2020.0388
Author Affiliations
Ahmad I: Department of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan [ORCID]
Ayub A: US Pakistan Center for Advanced Studies in Energy, National University of Sciences and Technology, Islamabad 44000, Pakistan
Kano M: Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan [ORCID]
Cheema II: Department of Chemical, Polymer and Composite Materials Engineering, University of Engineering and Technology, Lahore (New Campus) 39021, Pakistan [ORCID]
Ayub A: US Pakistan Center for Advanced Studies in Energy, National University of Sciences and Technology, Islamabad 44000, Pakistan
Kano M: Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan [ORCID]
Cheema II: Department of Chemical, Polymer and Composite Materials Engineering, University of Engineering and Technology, Lahore (New Campus) 39021, Pakistan [ORCID]
Journal Name
Processes
Volume
8
Issue
2
Article Number
E243
Year
2020
Publication Date
2020-02-20
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr8020243, Publication Type: Review
Record Map
Published Article
LAPSE:2020.0388
This Record
External Link
doi:10.3390/pr8020243
Publisher Version
Download
Meta
Record Statistics
Record Views
586
Version History
[v1] (Original Submission)
Apr 14, 2020
Verified by curator on
Apr 14, 2020
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2020.0388
Original Submitter
Calvin Tsay
Links to Related Works