Smart aeration strategy for electroplating wastewater: Insights from a dynamic interaction networks based...

Area of Science:

• Environmental Engineering
• Wastewater Treatment
• Industrial Process Control

Background:

• Existing aeration control methods for electroplating wastewater are often static or opaque, failing to address dynamic interactions.
• Current models lack interpretability and struggle to optimize complex processes effectively.

Purpose of the Study:

• To develop a data-driven framework for adaptive aeration control in electroplating wastewater treatment plants.
• To identify key process variables and effluent-specific control targets for optimized aeration strategies.

Main Methods:

• Integration of sliding-window correlation networks, SHAP-enhanced XGBoost models, and SHAP-guided Bayesian optimization.
• Analysis of dynamic interactions between process variables and effluent quality indicators.
• Development of interpretable predictive models for effluent parameters (COD, NH3-N, TN, TP).

Main Results:

• Network analysis revealed dynamic changes in aeration-effluent coupling over time.
• XGBoost models demonstrated high predictive accuracy (R² 0.951-0.998) for key effluent parameters.
• SHAP analysis identified influent flow rates (Flow_H, Flow_F) as critical predictors for effluent quality.

Conclusions:

• Differentiated, zone-specific aeration strategies are necessary, rather than a single plant-wide setting.
• The developed framework provides a transparent, data-driven approach for adaptive and energy-efficient aeration management.
• This method supports reliable effluent quality compliance and reduces the environmental footprint of industrial wastewater discharge.