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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Multi-agent large language model frameworks: Unlocking new possibilities for optimizing wastewater treatment
Samuel Rothfarb1, Mikayla Friday1, Xingyu Wang1
1School of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut, 06269, USA.
Environmental Research
|March 16, 2025
Summary
Multi-agent Large Language Models (LLMs) offer advanced wastewater treatment plant (WWTP) control by integrating diverse data for better decision-making. This AI approach enhances operational efficiency and adaptability beyond traditional methods.
Area of Science:
- Environmental Engineering
- Artificial Intelligence
- Wastewater Treatment
Background:
- Wastewater treatment plants (WWTPs) face operational challenges due to complex, dynamic interactions.
- Traditional models like Activated Sludge Models (ASMs) and machine learning algorithms (MLAs) struggle with unstructured, multimodal WWTP data.
Keywords:
Engineering system complexityHuman-data interactionLarge language model (LLM)Multi-agent frameworkReal-time decision makingWastewater treatmentMore Related Videos
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