Agent-Reactor Integration for Intelligent Wastewater Treatment: Experimental Validation and Interpretability of...

Area of Science:

• Environmental Engineering
• Artificial Intelligence
• Process Control

Background:

• Reinforcement learning (RL) offers promise for biological nutrient removal (BNR) but lacks real-world validation.
• Existing RL applications in BNR are limited to simulations, hindering practical adoption and operator trust due to their black-box nature.

Purpose of the Study:

• To integrate RL agents with physical bioreactors for real-time control of BNR processes.
• To demonstrate the superiority of RL-based control over conventional methods in managing disturbances.
• To enhance the interpretability and transparency of RL decision-making in wastewater treatment.

Main Methods:

• Developed a reactor-agent integration for direct physical bioreactor-RL agent interaction.
• Implemented RL-based control strategies to manage influent disturbances and optimize operational parameters.
• Employed a novel analysis framework combining controller-action visualization, surrogate decision trees, Sobol sensitivity analysis, and decision-trajectory analysis for policy interpretation.

Main Results:

• RL-based control demonstrated superior performance in managing influent disturbances compared to knowledge-based control.
• Achieved approximately 30% reduction in nitrogen species exceedance and 36.5% decrease in operational costs.
• The analysis framework successfully translated the RL agent's black-box policy into an interpretable and auditable control strategy.

Conclusions:

• Agent-based intelligent control is feasible, robust, and transparent for real-world BNR processes.
• The developed interpretability framework bridges algorithmic intelligence with process engineering transparency.
• This approach paves the way for the reliable deployment of RL in full-scale wastewater treatment plants (WWTPs).