In this study, numerical simulations of a gas-solid fluidized bed reactor involving a two-fluid Eulerian multiphase model and incorporating the Kinetic Theory of Granular Flow (KTGF) for the solids phase have been performed using a commercial Computational Fluid Dynamics (CFD) software. The fluidized bed setup consists of 1,5 m height and 0,2 m diameter in which a series of experiments were performed using Helium tracer to determine the Residence Time Distribution (RTD) at various normalized velocities i.e., with different degrees of gas-solids mixing. Both 2D and 3D simulations of the fluidized bed reactor are performed. The main purpose of this study is to understand the hydrodynamic behavior of a gas-solid fluidized bed reactor through a framework of Eulerian multiphase model and to analyze hydrodynamic behavior of the gas-solids mixing.
Abstract
In this study, numerical simulations of a gas-solid fluidized bed reactor involving a two-fluid Eulerian multiphase model and incorporating the Kinetic Theory of Granular Flow (KTGF) for the solids phase have been performed using a commercial Computational Fluid Dynamics (CFD) software. The fluidized bed setup consists of 1.5 m height and 0.2 m diameter in which a series of experiments were performed using Helium tracer to determine the Residence Time Distribution (RTD) at various normalized velocities i.e., with different degrees of gas-solids mixing. Both 2D and 3D simulations of the fluidized bed reactor are performed. The main purpose of this study is to understand the hydrodynamic behavior of a gas-solid fluidized bed reactor through a framework of Eulerian multiphase model and to analyze hydrodynamic behavior of the gas-solids mixing. As a first approach, the CFD model is validated using the experimental results of the residence time study. The numerical results of RTD corresponded well with the experimental findings indicating that the CFD model can be used to predict the performance of the fluidized bed reactor.
Keywords: Computational Fluid Dynamics, fluidized bed, residence time distribution, gas-solids mixing, turbulence
1 INTRODUCTION
Fluidization is the operation by which solid particles change into a fluid-like state through suspension in a gas or liquid. The process of fluidization can be described basically as supplying a flow of gas through a bed of granular material at an adequate velocity such that the granular bed acts as a fluid (Gidaspow, 1994; Richardson et al., 2008). Fluidized beds are common in industry and are used for catalytic reactions, granulation, particle coating processes, heating/cooling, drying, mixing, (Kunii and Levenspiel, 1991). Gas-solid fluidized beds are advantageous for many processes involving heat and/or mass transfer between phases. They provide efficient mixing, which results in excellent gas-solid contacting and relatively uniform temperature/concentration profiles within the bed (Cui and Grace 2007; Gidaspow 1994). Indeed, the need for cleaner and sustainable energy source has led to the development of biomass gasifiers which employs fluidized bed technology, as a promising approach, given its rapid biomass heating, effective heat and mass transfer and uniform reaction temperature(Salaices et al., 2010). Understandably, the physics behind fluidization indicates the importance of variables such as pressure drop, minimum fluidization velocity, solid volume fraction profile, particle velocity profile (Benzarti et al., 2012; Taghipour et al., 2005). Usually, fluidized bed reactors are chaotic in nature (Li et al., 2009). This is indicated by a turbulent fluidized bed. In addition, a fluidized bed is a medium to carry out a chemical reaction involving gas and solid. The main reason for choosing the fluidized bed for synthesis of solid catalyzed gas phase reactions is the demand for better temperature control of the reaction zone, and the conditions in fluidized bed reactors being near isothermal.
Nowadays, Computational Fluid Dynamics (CFD) has become a powerful tool for understanding the complex phenomena between gas and solid particle phases in the fluidized bed (Hartge et al., 2009; Wang et al., 2010; Xue et al., 2011). CFD is based on solving Navier-Stokes equations and has become a powerful tool for research and development in multiphase flow systems. Computational resources have increased substantially at sharply decreasing costs, allowing the acquisition of detailed computational information about reactions and flows at a fraction of the cost of the corresponding experiments (Dutta et al., 2010). It is possible to define a computational domain in which the geometry of the fluidized bed reactor can be incorporated and CFD approach can be used to solve the governing Navier-Stokes equations, using appropriate initial and boundary conditions. The results obtained from the numerical model can then be compared to experimental data for a necessary validation. Since computational assets have expanded significantly at forcefully diminishing costs, specified computational data about the flow can these day be acquired even at small amount of the expense relating to experimental (Xia & Sun, 2002). The transient CFD simulations are performed to suggest on the usability of the multiphase approach for the prediction of the solid phase mixing and residence time distribution in the riser (Andreux, et al., 2008). Transient solution is always necessary because of unsteady state nature of fluidized bed. CFD is useful in understanding the quantitative hydrodynamics of fluidization and is needed for the design and scale-up of efficient reactors in several processes industries (Ding & Gidaspow, 1990).
The scientific approach to evaluate the performance of a distributor and overall mixing behavior of a fluidized bed is to measure and analyze the residence time distribution (RTD) of the solid phase (Pant et al., 2014). The gas mixing in circular fluidized bed risers is evaluate as overall behavior. The overall mixing behavior has been studied by measuring the residence time distribution of a gas tracer injected at the feed inlet (Mahmoudi et al., 2010). The knowledge of RTD function is very important requirements for the optimization of the operating parameters and equipment configuration (Idakiev & Morl, 2013). The tracer is introduced at the inlet and monitored at outlet of the column. The concentration of tracer is introduced as a pulse into a fluidized bed column at the feed inlet. Then, the samples are collected at the exit at fixed time intervals until tracer concentration goes to zero at the outlet (Lopez-Isunza, 1975). Using CFD, the flow of inert tracer particles is calculated using species transport equation, where the diffusion coefficient of particles i.e. the necessary parameter indicating particle diffusion capacity is investigated (Hua et al., 2014). Typically, in a CFD study the experimental studies are conducted on a small-scale fluidized bed and numerical codes are used to model the experimental setups and validate the experimental results. The main objective of this work is to uses the CFD code ANSYS FLUENT to simulate the hydrodynamic behavior of a fluidized bed. A first approach of the full- fledged CFD model is the validation of the model with the experimental information. Due to the availability of residence time data, the CFD model is first validated with the RTD information obtained experimentally. The present study is divided into two main approaches: validation of the 2D and 3D simulation of gas-solid fluidized bed with the RTD data and a subsequent study to identify the flow patterns of gas-solid flow in a turbulent fluidized bed. The numerically predicted RTD results are compared with the experimental results of Lopez-Isunza (1975). The target is to make a meaningful comparison between the predictions of the RTD at three different airflow rates that given the normalized velocity ( U 0/ Umf = 9.5;8.0 and 6.4) . Note that the minimum fluidization velocity is 0.634 cm/s.
2 COMPUTATIONAL FLUID DYNAMICS (CFD) MODEL
2.1 Geometry and mesh
For the CFD simulations, initial mesh needs to be created. This mesh creates a geometry in which the calculations occur and furthermore is divided into several volumes according to the finite- volume methodology. These calculations are governed by the so-called Reynold's averaged Navier-Stokes (RANS) equations. A fluidized bed with a height of 1.5 m and a diameter of 0.2 m is simulated. The particle diameter used in the gas-solid fluidized bed is between 175—200 pm and falls in the Geldart group B particles (Geldart, 1973). The geometry is made in both 2D and 3D. The mesh consists of 12357 elements in 2D and 35616 elements in 3D respectively. This mesh of the fluidized bed is shown by figure 2.1 as follow in 2D and 3D.
Figure 2.1. Mesh of the Fluidized bed geometry for the study, shown in 2D and 3D respectively.
As seen in figure 2.1, the initial bed height is fixed at 0.5 m. The solid particles can be seen over the distributor plate. The gas inlet is at the bottom and the solids and gas outlet is the top. A tracer monitoring point is also at the top outlet.
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- Quote paper
- Baru Debtera (Author), 2021, Computational Fluid Dynamics (CFD) Simulation of a Gas-Solid Fluidized Bed. Residence Time Validation Study, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/1151429
