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

Lin Feng Yu1, Yu Qi Jin1, Miao Xin Yuan2

  • 1MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Institute of Geotechnical Engineering, Zhejiang University, Hangzhou 310058, China.

Waste Management (New York, N.Y.)
|May 14, 2025
PubMed
Summary

This study developed a model to optimize air classification of landfilled Municipal Solid Waste (MSW), improving resource recovery by accounting for waste

Keywords:
Air classificationDimensionDrag coefficientLandfilled municipal solid wasteMonte Carlo procedureSpheroid model

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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 (RL) 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.