What question did this study set out to answer?

The aim is to enhance wastewater treatment via reinforcement learning with operator trust and interpretability.

March 13, 2026Environmental Science & Technology0 citations

Agent–Reactor Integration for Intelligent Wastewater Treatment: Experimental Validation and Interpretability of Reinforcement-Learning-Based Control

ZZZ. A. ZhuShenzhen Third People’s Hospital ZYZhengxin YuBeijing Institute of Water YFYingzheng FanState Key Laboratory of Pollution Control and Resource Reuse

Key Points

The aim is to enhance wastewater treatment via reinforcement learning with operator trust and interpretability.
Integrated physical bioreactors with reinforcement learning agents
Conducted experiments to compare RL-based control with conventional strategies
Analyzed control actions using visualization and sensitivity analysis
Achieved 30% reduction in nitrogen species exceedance under short-term perturbations
Realized a 36.5% decrease in operational cost through coordinated adjustments
Provided interpretability for RL control strategies, enhancing process engineering transparency

Abstract

Intelligent control and optimization by reinforcement learning (RL) agents have emerged as a promising framework for biological nutrient removal (BNR) processes. However, most existing studies remain confined to simulation environments, limiting their credibility and engineering relevance. In addition, the black-box nature of RL agents hinders operator trust. This study establishes a reactor-agent integration that enables direct interaction between physical bioreactors and an RL-based agent, demonstrating that agent-based control outperforms conventional strategies in managing influent disturbances. Experimental results show that, under short-term perturbations, RL-based control achieved approximately 30% reduction in nitrogen species exceedance compared with knowledge-based control, while simultaneously realizing a 36.5% decrease in operational cost via coordinated adjustment of dissolved oxygen (DO) set points and internal mixed liquor recirculation. To enhance interpretability, this study proposes an analysis framework that bridges algorithmic intelligence with process engineering transparency. It combines controller-action visualization, surrogate decision trees, Sobol sensitivity analysis, and decision-trajectory analysis to elucidate the RL agent's decision logic and relate it to process kinetics and control principles. These insights transform the RL policy from a black box into an interpretable, process-consistent, and auditable control strategy. Overall, the results demonstrate the feasibility, robustness, and transparency of agent-based intelligent control under real-world conditions, paving the way for its reliable deployment in full-scale WWTPs.

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