Chowdhury, Wasif Shafaet, Yan, Yong (2023) Applications of artificial intelligence to instrumentation systems for monitoring complex industrial processes. Cybernetics and Intelligence, . (In press) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:104183)
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Abstract
Instrumentation and Measurement (I&M) is a field which is constantly developing due to the emergence of new technologies. In recent years, with the rapid development in computer hardware and computational power, Artificial Intelligence (AI) has demonstrated remarkable successes in data analytics, thus offering new paradigm for the design and applications of new instruments and measurement systems. The applications of AI to I&M have made measurements of some quantities in industry possible or more cost-effective. This paper presents a review of recent AI based methods applied in different aspects of instrumentation systems for monitoring complex industrial processes with a particular focus on multiphase flow metering, combustion monitoring as well as carbon dioxide flow measurement under carbon capture and storage conditions. This review also explores how AI is playing an important role in expanding knowledge across all spectrum of I&M. Trends and future developments of AI methods in the field of I&M are also discussed.
Item Type: | Article |
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Uncontrolled keywords: | Artificial intelligence; machine learning; multiphase flow metering; combustion monitoring; carbon capture and storage |
Subjects: |
T Technology T Technology > TA Engineering (General). Civil engineering (General) T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Funders: | University of Kent (https://ror.org/00xkeyj56) |
Depositing User: | Yong Yan |
Date Deposited: | 05 Dec 2023 07:41 UTC |
Last Modified: | 14 Feb 2024 09:51 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/104183 (The current URI for this page, for reference purposes) |
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