A Study of gas streaming in deep fluidized beds

Recent studies have shown that, in a sufficiently deep gas-solid fluidized bed of Geldart A particles, gas streaming may occur allowing gas to bypass a large portion of the particle bed. Since this is a newly observed phenomenon in fluidized beds, there is uncertainty and lack of information about the various aspects of the streaming flow. The objective of the current project is to investigate the streaming phenomenon with a combination of experimentation and modeling. In the experimental part, pressure fluctuations as a measure of the fluidized bed hydrodynamics were used to study the influence of different parameters on the behavior of a deep fluidized bed. Pressure fluctuations have been measured at 8 axial locations from 4 to 150 cm above the gas distributor for bed depths and gas velocities ranging from 0.4 to 1.6 m and 0.04 to 0.20 m/s (equal to 10 to 50 times minimum fluidization velocity), respectively. Two particle size distributions with Sauter mean diameters of 48 µm and 84 µm and two distributor plates with differing percentage open area were also tested for each bed depth and gas velocity. Analysis of pressure fluctuations in the time and frequency domains, in combination with visual observations revealed that streaming flow emerges gradually at bed depths greater than 1 m. Increased gas velocity and fines content act to delay the onset of streaming, but can not completely eliminate it over the range of velocities examined. The two different distributor designs had no measurable effect on the streaming flow. The results of this study are provided in the first section of the present report. In order to further investigate the nature of streaming flow, several cases of forced streams and jetting flows were designed and conducted, in addition to the natural streaming flow in deep beds. Results indicated that the natural streaming most closely resembles the imposed stream which not only the imposed stream, but additional gas added through the distributor. The case of jet flows with no additional gas resembles the severe streaming that might happen in very deep beds with the existence of completely non-fluidized regions. Application of supporting jets in addition to the main gas flow could enhance the fluidization quality to some extent, however, not enough to provide a normal fluidization. Wavelet analysis of the pressure fluctuations showed that in deep fluidized beds, bubbling activity with the typical dominant frequency coexist with the streaming flow, with a minor contribution. Wavelet findings suggested that the streaming flow can be considered to form by increasing the relative importance of one available stream of bubble activity with increasing bed depth. The results of this study are provided in the second section of this report. Further study of the streaming flow was undertaken with computational fluid dynamic (CFD) simulation of the deep fluidized bed. CFD simulation of fine Geldart A particles has met with challenges in the open literature and various modifications have been proposed to be able to model fluidized beds of these particles. In the present work, the commercial CFD codes FLUENT and MFIX were initially tested for the modeling of deep fluidized bed of Geldart A particles. However, simulation results did not show any sign of streaming flow in the fluidized bed. Subsequently, the commercial CFD code BARRACUDATM that has been claimed by the developers to be appropriate for this purpose, was tested. Due to the lack of data on the performance of this code, a simple case of modeling a freely bubbling fluidized bed of Geldart A particles was attempted first. For this purpose, four different simulation cases, which included three different numerical grid sizes and two drag models with a realistic particle size distribution were designed and tested. The simulated bed expansion, bubble size distribution, rise velocity and solid fraction were compared with commonly accepted correlations and experimental data from the literature. The results showed a promising predictive capability of the code without the need for modifying the drag model or other constitutive relations of the model. The third section of the report presents the simulation results of this study. The BARRACUDA code was then used for simulating the deep fluidized bed of Geldart A particles. However, similar to the previous CFD codes tested, instead of streaming flow, bubbling fluidization was predicted. Therefore, a phenomenological model was developed to better understand the streaming flow. According to the model results, the stream represents a zone of much lower pressure drop compared to other parts of the bed, which can be a possible reason for the formation and stability of the streaming flow inside the fluidized bed. The model results showed that increasing the bed depth enhances the streaming flow, while increasing the gas velocity improves the uniformity of the bed and decreases the streaming severity. Streaming flow was found to be less severe for larger particle sizes. All of these trends are in conformity with the experimental results. These findings provide the content of the fourth and final section of this report

A Modeling Study of Gas Streaming in a Deep Fluidized Bed of Geldart A Particles

Gas streaming has been modeled in a deep fluidized bed of 5 m depth and 0.3 m inside diameter. The model results suggest that the lower pressure drop of the stream zone compared to the remainder of the bed is the reason for severe streaming flow in deep beds. The effects of different parameters such as bed depth, gas velocity and particle size on the severity of the streaming flow are also evaluated with the model

Prediction of the Dynamics of a Fluidized Bed Reactor using Artificial Neural Networks

The dynamic behavior of fluidized bed has been studied based on the chaos theory. The experiments were done in a fluidized bed of 0.15 m diameter using an optical fiber probe. The interval between successive clusters in the fluidized bed were calculated from the time series signals and proved to be chaotic by calculating the correlation dimension. An artificial neural network (ANN) was adapted and trained to predict the generated time series. The ANN results were compared with the predictions of the k-Nearest Neighbor (kNN) method to show the superiority of ANN in chaotic time series prediction

A New Approach for Modeling of a Fluidized Bed by CFD-DEM

Numerical studies of 3D cylindrical fluidized bed by means of combined computational fluid dynamics (CFD) and discrete element method (DEM) were carried out. For motion of particles, Newton\u27s second law and 3D compressible Navier-Stokes equations in generalized curvilinear coordinates in its conservative form were used. Navier-Stokes equations were solved with high order compact finite difference scheme by fully implicit flux decomposition method. Non-reflecting boundary conditions (NRBC) were used for the outflow boundary. The convergence of this method, especially at high Reynolds number, is significantly better than the SIMPLE method

A new approach for modeling of a fluidized bed by CFD-DEM

Numerical studies of 3D cylindrical fluidized bed by means of combined computational fluid dynamics (CFD) and discrete element method (DEM) were carried out. For motions of particles, Newton's second law and 3D compressible Navier-Stokes equations in generalized curvilinear coordinates in its conservative form were used. Navier-Stokes equations were solved with this high order compact finite difference scheme by fully implicit flux decomposition methods. Non-reflecting boundary conditions (NRBC) were used for the outflow boundary. The convergence of this method, especially at high Reynolds number, is significantly better than the SIMPLE method

Fuel xxx (2012) xxx–xxx Contents lists available at SciVerse ScienceDirect Fuel

journal homepage: www.elsevier.com/locate/fuel Study of factors affecting syngas quality and their interactions in fluidize