Data-driven differentiable model for dynamic prediction and control in wastewater treatment.

Area of Science:

• Environmental Engineering
• Computational Science
• Artificial Intelligence

Background:

• Urban wastewater treatment plants (WWTPs) face challenges in operational efficiency and sustainability.
• Increasing urbanization and stricter environmental standards exacerbate these issues.
• Traditional modeling approaches struggle to capture complex wastewater dynamics efficiently.

Purpose of the Study:

• To introduce an innovative continuous-time neural framework for enhanced modeling of sewage treatment processes.
• To address the dual challenges of operational efficiency and sustainable development in WWTPs.
• To reduce computational demands and memory usage in wastewater treatment modeling.

Main Methods:

• Implementation of a continuous-time neural framework based on Neural Ordinary Differential Equations (Neural ODEs).
• Analysis of operational data from three full-scale WWTPs over one year.
• Integration with reinforcement learning for control strategy optimization.

Main Results:

• Achieved superior prediction accuracy (R² > 0.95) with reduced computational demands (95% reduction).
• Significantly reduced memory usage (from 111.88-12,484.59 MB to 17.74-50.92 MB).
• Demonstrated robust performance with up to 30% missing data and a 21.9% reduction in aeration energy consumption.

Conclusions:

• The developed framework offers a novel paradigm for intelligent wastewater management.
• Optimizes operational efficiency and promotes environmental sustainability in WWTPs.
• Provides interpretable feature attribution for uncovering new process insights.