Computational fluid dynamics (CFD) simulations of large-scale furnaces or reactors for thermal conversion of solid fuels remains challenging partially due to the high computational cost related to the particle sub-models. Owing to the thermally thick nature, it is particularly expensive to simulate the conversion of large fuel particles such as biomass particles. To address this issue, a fast-solving particle model was developed in this work with special attention to the computational efficiency. The model spatially discretizes a fuel particle in one homogenized dimension. The conversion process of the fuel particle is treated as a reactive variable-volume one-dimensional transient heat conduction problem. The model also utilizes several features that are typically found in sharp interphase-based models to reduce the computational cost. Validation of the model was carried out by comparing with experimental results under both pyrolysis and combustion conditions. The accuracy and computational efficiency of the model was thoroughly examined by varying the degrees of temporal and spatial discretization. It was found that the model well predicted pyrolysis and combustion of a single biomass particle within a broad range of temporal and spatial discretization. The time used to simulate the conversion of a biomass particle using the developed model can be more than one order of magnitude smaller than the conversion process itself. It was also revealed that a well-predicted conductive heat transfer inside the particle is essential for a precise simulation of the drying and devolatilization process. The char conversion process, however, is less sensitive to the external heat transfer as it is mainly controlled by the mass diffusion process. Further studies showed that a time step of 1 x 10(-3) s and a spatial discretization of 20 cells were sufficient for simulating the conversion of typical fuel particles in grate-fired and fluidized-bed furnaces. We also demonstrated that when the particle model was implemented in a CFD solver, only 2.2% of computational overhead was introduced by the model. As the model can efficiently employ fixed time stepping, optimal load balancing during parallel computing of many simultaneous conversion processes becomes trivial. This performance opens up new possibilities for treating fuel polydispersity in Eulerian CFD simulations of biomass conversion. (C) 2020 The Combustion Institute. Published by Elsevier Inc. All rights reserved.