Online learning-empowered smart management for A²O process in sewage treatment processes

Area of Science:

• Environmental Engineering
• Artificial Intelligence
• Wastewater Treatment

Background:

• Global water pollution necessitates intelligent sewage treatment management.
• Existing AI models struggle with complex, nonlinear wastewater processes due to reliance on static historical data.
• Offline machine learning lacks adaptability to changing environmental conditions.

Purpose of the Study:

• To design an intelligent management system for automatic monitoring and decision-making in sewage treatment.
• To develop an online learning-empowered smart management system (OL-AP) for the A2/O process.
• To overcome limitations of traditional AI models in dynamic wastewater treatment scenarios.

Main Methods:

• Implemented automatic data collection using an Internet of Things (IoT) sensor network.
• Trained a prediction model using preprocessed historical sewage treatment data.
• Employed online learning to retrain and optimize the model with real-time data feedback.

Main Results:

• Achieved automatic data collection and model training with improved evaluation indexes.
• Demonstrated the effectiveness of online learning in adapting AI models to dynamic wastewater treatment conditions.
• Optimized model parameters through continuous retraining based on real-time operational data.

Conclusions:

• Online learning significantly enhances the adaptive capacity of AI models in sewage treatment.
• The developed OL-AP system offers a more dynamic and intelligent approach to managing wastewater treatment processes.
• This methodology provides a robust framework for intelligent environmental management systems.