Machine learning-based optimization of biogas and methane yields in UASB reactors for treating domestic wastewater

Area of Science:

• Environmental Engineering
• Biotechnology
• Artificial Intelligence

Background:

• Up-flow anaerobic sludge blanket (UASB) reactors are crucial for domestic wastewater treatment and bioenergy recovery.
• Optimizing biogas and methane production in UASB reactors is essential for sustainable energy generation.
• Predictive modeling can enhance the efficiency of wastewater treatment processes.

Purpose of the Study:

• To optimize biogas and methane production from UASB reactors treating domestic wastewater.
• To evaluate the performance of eXtreme Gradient Boosting (XGBoost) and XGBoost-PSO machine learning models for predicting gas yields.
• To identify key operational variables influencing biogas and methane production.

Main Methods:

• Utilized Up-flow anaerobic sludge blanket reactors for treating synthetic and real domestic wastewater.
• Employed eXtreme Gradient Boosting (XGBoost) and XGBoost integrated with Particle Swarm Optimization (XGBoost-PSO) machine learning models.
• Trained and validated models using operational variables such as COD, pH, flow rate, and retention time, monitoring gas production.

Main Results:

• The XGBoost-PSO model significantly outperformed the standard XGBoost model in both training and testing phases.
• XGBoost-PSO achieved high accuracy for biogas prediction (R²=0.9832 training, R²=0.9404 testing) and methane prediction (R²=0.9942 training, R²=0.9717 testing).
• Key operational variables influencing gas production were identified and modeled effectively.

Conclusions:

• The integration of AI-driven approaches, specifically XGBoost-PSO, offers a powerful tool for optimizing bioenergy recovery in wastewater treatment.
• Machine learning models can accurately predict biogas and methane production, enabling better process control and efficiency.
• This study demonstrates the potential of advanced computational methods to enhance sustainable wastewater management and energy production.