Auditable Automation of Activated Sludge Modeling for Wastewater Treatment Diagnosis Using LLM-Agents

Building activated sludge models (ASMs) still depends heavily on expert choices about boundaries, components, and reactions. We developed AutoWWTP-ASM, an auditable large-language-model agent (LLM-Agent) workflow for library-constrained ASM configuration, execution, and calibration.

The workflow converts wastewater-process descriptions into structured configurations and combines a predefined ASM library with boundary specification and optional human review. In a coupled carbon−nitrogen−phosphorus task, AutoWWTP-ASM instantiated a model containing 21 state components and 41 biochemical reactions. Across 37 evaluation tasks, the average accuracy scores of ten LLM backbones ranged from 0.596 to 0.627, indicating a narrow range of backbone-level performance within the tested workflow.