Eulerian-Eulerian and Eulerian-Lagrangian models are widely applied to numerically investigate dense solid-gas fluidized bed systems. However, only a few studies in the existing literature focus on a detailed analysis and comparison of the different modeling approaches. Therefore, the objective of this study is to investigate the Two Fluid Model (TFM), the Dense Discrete Phase Model (DDPM), and the Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) with respect to a comprehensive independency study, a sensitivity analysis of the respective submodels and model parameters, a comparison of the simulation results between the different modeling approaches, as well as their computational demand. The simulations represent a test case of a pilot plant scale reactor (inner diameter up to 387 mm, height 900 mm), which is used within a thermochemical energy storage system (CaO + H2O <--> Ca(OH)(2)). Monodisperse Geldart B powder (particle diameter 200 mu m, particle density 2500 kg/m(3), sphericity factor 0.8) is fluidized with water vapor (ideal gas, temperature 400 degrees C, inlet velocity 0.495 m/s). Grid size, fluid and particle time step, and particle clustering are considered for the independency study. Different wall boundary conditions and calculation methods for the granular temperature are investigated for the TFM. For the DDPM, different wall boundary conditions are analyzed as well, while for the CFD-DEM, simulations with two different DEM submodels (Hertzian and Hookean collision model) and a sensitivity analysis of the respective model parameters are included. Results show that the TFM and the CFD-DEM are able to predict qualitatively and quantitatively similar gas and particle flow fields, if TFM model parameters are set appropriately. However, a quite different pressure drop and bed expansion is observed for each model. The DDPM does not result in realistic flow fields for the investigated test case, despite predicting a pressure drop and bed expansion similar to the TFM. The presented findings lead to the conclusion that model validation should not only be based on global observable values like pressure drop and bed expansion, but also on local particle flow fields and bubbling behavior. (C) 2018 Elsevier Ltd. All rights reserved.