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• W4377018082 endingPage "106817" @default.
• W4377018082 startingPage "106817" @default.
• W4377018082 abstract "When planning the development of the energy sector, significant attention is given to the energy from the renewable sources, amongst which the biomass has an important role. Computational fluid mechanics and machine learning models are the powerful and efficient tools which allow the analysis of various heat and mass transfer phenomena in energy facilities. In this study, the in-house developed CFD code and machine learning models (Random Forest, Gradient Boosting and Artificial Neural Network) for predicting the biomass trajectories, particle mass burnout and residence time in a swirl burner reactor are presented. Pulverized biomass combustion cases (fine straw, pinewood and switch grass) with various mean diameters (ranging between 60 and 650 μm) and different shape factors (within the range 0–1) are considered. The results of numerical simulations revealed a noticeably nonlinear dependence between the input values (particle types, sizes and shapes) and the output values (particle trajectories, mass burnout and residence time), mostly due to the complex swirling flow in the reactor. For particles with the mean diameters within the ranges considered, the mass burnout of particles generally decreases as the biomass particle shape factor increases. The residence time of pulverized biomass in the reactor shows in most cases a decreasing trend as the particle shape factor increases. Artificial Neural Network showed the best predictions for both particle mass burnout (RMSE = 0.083 and R2 = 0.937) and particle residence time (RMSE = 1.145 s and R2 = 0.900), providing the reliable assessment of these important indicators in the combustion process." @default.
• W4377018082 created "2023-05-19" @default.
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• W4377018082 date "2023-07-01" @default.
• W4377018082 modified "2023-09-26" @default.
• W4377018082 title "Effects of biomass particles size and shape on combustion process in the swirl-stabilized burner reactor: CFD and machine learning approach" @default.
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• W4377018082 doi "https://doi.org/10.1016/j.biombioe.2023.106817" @default.
• W4377018082 hasPublicationYear "2023" @default.
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