A multi-agent AI reinforcement-based digital multi-solution for optimal operation of a full-scale wastewater treatment plant under various influent conditions
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Agentic AI is characterized by multi-step, multi-layered tasks over extended time frames.
“Multi-step, multi-layered tasks over extended time frames”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [63]
Multiagent systems play a role in the optimization of tasks spanning wastewater treatment, factory operations, predictive maintenance, and energy-aware task scheduling.
“Multiagent systems play a role in the optimization of a number of tasks spanning wastewater treatment, factory operations, predictive maintenance, and energy-aware task scheduling, among others [33,63].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [63]
In applications where action or intelligence is needed, such as for safety or efficiency management through automation with utilities, robotics or autonomous vehicles or processes and systems, agentic AI systems use action-based datasets to learn how to recognize anomalies, mitigate threats, or identify the best actions to optimize processes.
“In applications where action or intelligence is needed, such as for safety or efficiency management through automation with utilities, robotics or autonomous vehicles or processes and systems (such as supply chains), agentic AI systems use action-based datasets, which contain information about conducted actions, so they can learn how to recognize anomalies [117], mitigate threats, or identify the”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [63]
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References 50 · Cited by 36
- Authors
- Nam, K., Heo, S., Kim, S., & Yoo, C.
- Affiliations
- Kyung Hee University (KR)
- Corresponding author
- Yoo, ChangKyoo
- Venue
- Journal of Water Process Engineering
- Publisher
- Elsevier BV
- Published
- 2023-04
- Volume
- 52
- Pages
- 103533
- ISSN
- 2214-7144 (ISSN-L)
- OpenAlex
- W4320474821
- Cited by
- 36
- References
- 50
- Citations by year
- 2026: 6 · 2025: 17 · 2024: 10 · 2023: 3
- FWCI
- 2.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Water Process Engineering (publishedVersion)
- Funders
- Ministry of Science, ICT and Future Planning (2021R1A2C2007838)
- National Research Foundation of Korea
- Ministry of Environment
- Ministry of SMEs and Startups (S3301144)
- Topics
- Physical Sciences › Environmental Science › Pollution › Wastewater Treatment and Nitrogen Removal
- Physical Sciences › Environmental Science › Waste Management and Disposal › Water Quality Monitoring and Analysis
- Physical Sciences › Environmental Science › Waste Management and Disposal › Wastewater Treatment and Reuse
- Keywords
- wastewater treatment plant operation, multi-agent reinforcement learning, game abstraction, chemical oxygen demand, total nitrogen, sludge recycling, aeration energy reduction
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