The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes
Abstract
Over the past twenty years, numerous research outcomes have been published, related to the design and implementation of soft sensors. In modern industrial processes, various types of soft sensors are used, which play essential roles in process monitoring, control and optimization. Emerging new theories, advanced techniques and the information infrastructure have enabled the elevation of the performance of soft sensing. However, novel opportunities are accompanied by novel challenges. This work is motivated by these observations and aims to present a comprehensive review of the developments since the start of the millennium. While a few books and review articles are published on the related topics, more focus on the most up-to-the-date advancement is put in this work, from the perspective of systems and control.
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Cites
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“The model-driven family of soft sensors is most commonly based on first principle models [4].”
BackgroundMethod
Cited by
Perera, Y. S., Ratnaweera, D. A. A. C., Dasanayaka, C. H., & Abeykoon, C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105988 · Cited by 286
Guo, R., Liu, H., & Liu, D. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3297663 · Cited by 79
Pietrzak, P., & Wolkiewicz, M. (2021) · Electronics · doi:10.3390/electronics10151786 · Cited by 54
Metadata
- Authors
- Jiang, Y., Yin, S., Dong, J., & Kaynak, O.
- Venue
- IEEE Sensors Journal
- Published
- 2021-06-01
- Volume
- 21
- Issue
- 11
- Pages
- 12868-12881
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W3093656051
- Cited by
- 667
- References
- 83
- FWCI
- 33.90
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jiang, Y., Yin, S., Dong, J., & Kaynak, O. (2021). A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes. IEEE Sensors Journal. https://doi.org/10.1109/jsen.2020.3033153
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
