Investigates an indirect calibration method in DEM simulations to improve WtE operation, suggesting better energy recovery methods.
Waste-to-Energy (WtE) power plants recover energy from municipal solid waste (MSW) to produce electricity and heat, reduce disposal volumes, and mitigate potential health hazards. Energy recovery is mainly achieved through grate combustion, where mechanical components ensure adequate mixing and transport of MSW for complete combustion and continuous operation. However, the highly heterogeneous composition of MSW and the complexity of the solid combustion process limit electrical efficiency, motivating efforts to improve overall operational performance. Given the significant economic and technical effort associated with experimental investigations, numerical approaches such as Computational Fluid Dynamics and the Discrete Element Method (DEM) are increasingly employed to improve understanding of the governing physical phenomena and to evaluate potential optimizations. However, numerical methods require detailed input data to realistically represent the studied system. In the case of MSW, direct characterization and conventional calibration approaches are impractical due to the inherent heterogeneous nature of MSW. To address this limitation, the present work investigates an indirect calibration methodology for DEM simulations based on experimental data from a 35 MW th WtE power plant. The proposed approach incorporates internal functions to account for particle shrinkage and to ensure a realistic particle distribution along the grate, combined with a three-stage stochastic evaluation framework. The calibration process identifies parameter sets consistent with experimental observations, enabling a more representative description of MSW particle behavior in grate-based combustion systems. Suitable parameter ranges include a density of 650–750 kg/m 3 , friction coefficients of 0.6–0.7, a roll resistance of 0.4, and a stiffness fraction of 0.15. Methodological limitations and future opportunities for DEM-based waste modeling are also discussed.
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Rivas et al. (2026) studied this question.
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