MA Rui, YI Weiming, SHENG Yuwan, et al. Transient multiphysics coupled simulation of biomass fast pyrolysis in a down-tube reactorJ. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2026, 42(2): 291-300. DOI: 10.11975/j.issn.1002-6819.202505047
    Citation: MA Rui, YI Weiming, SHENG Yuwan, et al. Transient multiphysics coupled simulation of biomass fast pyrolysis in a down-tube reactorJ. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2026, 42(2): 291-300. DOI: 10.11975/j.issn.1002-6819.202505047

    Transient multiphysics coupled simulation of biomass fast pyrolysis in a down-tube reactor

    • A transient multiphysics numerical framework is developed to accurately describe the coupled heat transfer and fast pyrolysis behavior of biomass particles interacting with high-temperature ceramic balls in a down-tube reactor. The model is based on the Distributed Activation Energy Model (DAEM), which effectively captures the intrinsic heterogeneity of biomass pyrolysis kinetics. Multiple heat transfer mechanisms—including Hertzian contact conduction, gas-film conduction, convection, and radiation—are explicitly incorporated to establish a complete energy balance and a system of coupled ordinary differential equations governing particle-scale thermal and reaction dynamics.
      Thermogravimetric analysis (TGA) data obtained at different heating rates were used to calibrate the kinetic parameters. Gaussian, Lorentzian, and logistic functions were systematically compared to describe the activation energy distribution. Among them, the Lorentzian distribution provided the best agreement with experimental data, yielding a mean absolute error (MAE) of 0.011 and a root mean square error (RMSE) of 0.013, owing to its superior capability in representing the extended high-energy tail of biomass pyrolysis reactions.
      Simulation results indicate that biomass particles experience extremely rapid heating during the initial stage, with peak heating rates reaching 2.14 × 103 ℃·s−1. Despite the rapid attainment of thermal equilibrium, a pronounced delay in mass conversion was observed, revealing that fast pyrolysis under down-tube conditions is governed by intrinsic chemical kinetics rather than heat transfer limitations. Heat transfer analysis further shows that conduction (including direct solid–solid contact and gas-film pathways) and convection dominate the energy exchange process, collectively accounting for approximately 85~90% of total heat transfer. Radiation contributes a smaller fraction (10~15%) but remains non-negligible at elevated temperatures. The endothermic nature of biomass pyrolysis leads to cumulative energy consumption, which further prolongs the completion of conversion.
      A comprehensive parameter sensitivity analysis demonstrates that ceramic ball temperature and biomass particle radius are the most influential factors affecting heating rates and conversion efficiency. Increasing the ceramic ball temperature markedly accelerates particle heating and enables near-complete attainment of the theoretical conversion (~83.3%), whereas reducing particle size mitigates thermal inertia and shortens reaction lag. Reaction enthalpy and collision probability exert secondary effects, while the radiative view factor plays a minor role due to its limited contribution to overall heat transfer.
      The proposed DAEM-based multiphysics model provides both mechanistic insight and quantitative predictive capability for biomass fast pyrolysis in down-tube reactors. By accurately reproducing transient temperature evolution, mass conversion, and reaction rate characteristics, the framework offers practical guidance for reactor design and operation, including optimization of solid–solid contact efficiency, particle size selection, and external heating intensity. Owing to its modular structure, the model can be readily extended to other thermochemical conversion systems, such as fluidized beds or rotary kilns, through appropriate modification of boundary conditions. Future work will focus on coupling particle-scale dynamics with CFD-based reactor-scale simulations to further enhance predictive fidelity.
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