The Application of Reinforcement Learning to Pumps—A Systematic Literature Review
Reinforcement learning, a subset of machine learning in the field of engineering informatics, has revolutionized the decision-making and control of industrial pumping systems. A set of 100 peer-reviewed papers on the application of reinforcement learning to pumps, sourced from the Scopus database, w...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/13/6/480 |
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| Summary: | Reinforcement learning, a subset of machine learning in the field of engineering informatics, has revolutionized the decision-making and control of industrial pumping systems. A set of 100 peer-reviewed papers on the application of reinforcement learning to pumps, sourced from the Scopus database, were selected. The selected papers were subjected to bibliometric and content analyses. The existing approaches in use, the challenges that have been experienced, and the future trends in the field are all explored in depth. The majority of the studies focused on developing a control system for pumps, with heat pumps being the most prevalent type, while also considering their economic impact on energy consumption in the industry. Future trends include the use of Internet-of-Things sensors on pumps, a hybrid of model-free and model-based reinforcement learning algorithms, and the development of “weighted” models. Finally, ideas for developing a practical reinforcement learning-bundled software for the industry are presented to create an effective system that includes a comprehensive reinforcement learning framework application. |
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| ISSN: | 2075-1702 |