Developments to a landfill processes model following its application to two landfill modelling challenges.

Area of Science:

• Environmental Engineering
• Geotechnical Engineering
• Waste Management

Background:

• Landfill modeling is crucial for predicting waste behavior and environmental impact.
• Previous models lacked comprehensive simulation of solid, liquid, and gas phases.
• Blind modeling challenges (LMC1, LMC2) highlighted areas for improvement in landfill simulation.

Purpose of the Study:

• To describe developments in the Landfill model (LDAT) based on participation in the LMC1 and LMC2 modeling challenges.
• To calibrate LDAT using full datasets from laboratory and field-scale landfill test cells.
• To enhance the simulation of bio-chemical processes and waste phase transport within landfills.

Main Methods:

• Application of the LDAT model in predictive mode for blind landfill modeling challenges.
• Modularization of LDAT into linked sub-models for improved flexibility.
• Development of strategies for waste characteristic conversion and flexible degradation pathways.
• Integration of a chemical equilibrium model with solid waste dissolution.

Main Results:

• LDAT was successfully applied to laboratory (LMC1) and field-scale (LMC2) landfill test cells.
• Model developments improved the simulation of waste transport and bio-chemical behavior.
• Calibration with full datasets validated the model's predictive capabilities.
• Flexible degradation pathways allowed control over the CO2:CH4 ratio.

Conclusions:

• The developed LDAT model provides a robust tool for simulating landfill processes.
• Modularization and enhanced sub-models improve accuracy and flexibility in waste management simulations.
• The study demonstrates the value of blind modeling challenges in advancing landfill simulation technology.