Advanced methods to calculation of pressure drop during aeration in composting process

Area of Science:

• Environmental Engineering
• Waste Management
• Computational Modeling

Background:

• Composting municipal waste requires understanding air flow dynamics for efficiency.
• Predicting air flow resistance is crucial for optimizing aeration in composting beds.
• Organic fraction of municipal waste presents complex challenges for air flow management.

Purpose of the Study:

• To develop a predictive model for air flow resistance in organic waste composting.
• To identify key process parameters influencing air flow resistance.
• To validate the model's accuracy using statistical indicators.

Main Methods:

• Utilized organic fraction (<80mm) from municipal waste.
• Investigated hydraulic load (8.49–50.96 m³·m⁻²·h⁻¹), thickening coefficient (0.69–0.94), and airflow direction.
• Maintained material humidity at ~45% across varying bed heights and durations (19–25 days).
• Employed a Multi-Layer Perceptron (MLP/5-9-1) neural network for modeling.

Main Results:

• Selected an MLP/5-9-1 neural network model based on simulation analysis.
• Achieved a high coefficient of correlation (0.906) between predicted and real values.
• Standardized residuals ranged from 4.082 to 5.453, indicating model reliability.

Conclusions:

• The developed neural network model accurately predicts air flow resistance in organic waste composting.
• The model provides a valuable tool for optimizing aeration strategies in composting operations.
• This research contributes to more efficient and effective waste management through improved process control.