Bayesian Optimization-Enhanced Reinforcement learning for Self-adaptive and multi-objective control of wastewater...

Area of Science:

• Environmental Engineering
• Artificial Intelligence in Water Management
• Process Control

Background:

• Wastewater treatment plant (WWTP) operations face challenges with dynamic influent and multiple objectives.
• Optimizing activated sludge processes requires advanced control strategies.

Purpose of the Study:

• To develop and test an integrated Reinforcement Learning (RL) and Bayesian Optimization (BO) approach for WWTP control.
• To enhance the control of critical operational parameters in activated sludge processes.

Main Methods:

• Applied Reinforcement Learning (RL) algorithms to various activated sludge configurations.
• Integrated RL with Bayesian Optimization (BO) for enhanced control.
• Tested the approach on the A2O process.

Main Results:

• Achieved a 46% reduction in operational costs.
• Reduced energy consumption by 12%.
• Maintained compliance with effluent discharge standards.

Conclusions:

• The integrated RL-BO approach offers a practical solution for WWTPs.
• This method enhances treatment efficiency and reduces operational costs.
• It contributes to sustainable wastewater management practices.