Air classification efficiency evaluation of landfilled municipal solid waste using experiments and a probabilistic...

Area of Science:

• Waste Management
• Environmental Engineering
• Computational Fluid Dynamics

Background:

• Air classification of landfilled Municipal Solid Waste (MSW) is crucial for resource recovery.
• Challenges include waste heterogeneity and non-spherical particle shapes.
• Optimizing separation efficiency is vital for effective waste management.

Purpose of the Study:

• To develop a probabilistic framework integrating spheroid modeling and Monte Carlo methods for predicting and optimizing MSW air classification efficiency.
• To statistically characterize the morphology and density of landfilled MSW samples.
• To compare numerical model predictions with experimental results.

Main Methods:

• Statistical characterization of morphological (elongation, flatness, size) and density distributions of 381 and 184 landfilled MSW samples, respectively.
• Development of a numerical model using spheroidal particles with non-spherical drag coefficients, generated via random sampling.
• Integration of spheroid modeling with Monte Carlo procedures for predicting separation efficiency.

Main Results:

• The numerical model achieved a Root Mean Square Error (RMSE) of <0.13 in predicting separation indicators against experimental data.
• Landfilled MSW showed lower light fraction recovery (R_L) than fresh MSW due to increased density from organic matter degradation.
• Separation efficiency (E) exhibited velocity-dependent unimodal trends, with optimal performance identified at 15° airflow direction and 21.40 m/s.

Conclusions:

• The developed probabilistic framework accurately predicts air classification efficiency for landfilled MSW.
• Optimal separation parameters were identified, offering insights for apparatus design.
• This research provides a foundational method for advanced simulation studies in waste management.