A novel model-based adaptive control strategy for step-feed SBRs dealing with influent fluctuation

Area of Science:

• Environmental Engineering
• Wastewater Treatment Technologies
• Process Control

Background:

• Sequence Batch Reactors (SBRs) are widely used for wastewater treatment.
• Traditional control strategies often struggle with influent variability.
• Optimizing SBR operation is crucial for efficient nutrient removal.

Purpose of the Study:

• To develop and evaluate a novel model-based adaptive control strategy for SBRs.
• To compare the performance of adaptive control against fixed-parameters and statically optimal control.
• To assess the effectiveness of the control strategy under fluctuating influent conditions.

Main Methods:

• A fully coupled activated sludge model (FCASM) was employed for numerical calculations.
• Operating parameters were automatically adjusted based on real-time model predictions.
• The model-based adaptive control strategy was implemented and tested.

Main Results:

• The adaptive control strategy achieved high removal efficiencies for total nitrogen (TN) and total phosphorus (TP) above 84% and 98%, respectively.
• Compared to fixed-parameters control, TN removal efficiency improved by 25.11%.
• The strategy effectively managed influent fluctuations, maintaining high performance.

Conclusions:

• Model-based adaptive control offers a superior approach for SBR operation compared to traditional methods.
• This strategy enhances nutrient removal efficiency, particularly under variable influent conditions.
• The FCASM provides a robust foundation for optimizing SBR performance.