Advanced Graph–Physics Hybrid Framework (AGPHF) for Holistic Integration of AI-Driven Graph- and Physics- Methodologies to Promote Resilient Wastewater Management in Dynamic Real-World Conditions
Source abstract
Abstract
Wastewater treatment is evolving rapidly with the advent of advanced deep-learning AI, graph-based, and physics-informed approaches. This study integrates graph neural networks, physics-informed neural networks, and multi-agent reinforcement learning within a hybrid digital-twin framework, evaluated on multi-scale real-world datasets. The developed models achieved over 90% dissolved organic carbon removal and reduced aeration energy by up to 22% while maintaining process stability. The results demonstrate that graph–physics synergies not only boost operational efficiency but also reveal critical trade-offs between energy savings and hydraulic performance. Our findings establish a new benchmark for resilient, low-carbon wastewater treatment, highlighting the transformative role of data-driven system design.
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Research context
OpenAlex topics: Smart Grid Security and Resilience · Fuel Cells and Related Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, 17177 Solna, Sweden
Department of Industrial Design and Production Engineering, University of the Aegean, 82132 Chios, Greece
Department of Health Outcomes Research and Policy, Harrison College of Pharmacy, Auburn University, Auburn, AL 36849, USA
Department of Crop Sciences, College of Agricultural, Consumer and Environmental Sciences, University of Illinois Urbana–Champaign, Urbana, IL 61801, USA
Mayo Clinic Artificial Intelligence & Discovery, Rochester, MN 55905, USA
MLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, Greece
Informatics and Scientific Applications, Harvard University, Cambridge, MA 02138, USA
Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, 10623 Berlin, Germany
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