Investigation of Bubble Properties in a Bubbling Fluidized-Bed Gasification Reactor Using a Computational Particle Fluid Dynamic Model

Abstract

The conversion efficiency, operation, and design of bubbling fluidized bed (BFB) reactors depend on the bed dynamics behavior, which is significantly influenced by the bubble properties. To establish the best operating condition for efficient conversion, this study investigates the dynamics behavior of a BFB reactor using experimental measurements and computational particle-fluid dynamics simulation. The simulations account for particle size distribution and variation of particle properties used in the experiments to eliminate the possible effects on the bed behavior. Compared with a cold bed of similar biomass load and gas velocity, the results show that bubbles propagate with a wider distribution, a smaller size, and a higher frequency in the hot gasifying bed. The bubble diameter and amount of unconverted char particles increase with increasing air flow rate at a constant air−fuel ratio. Although the solid particle distribution over the bed can be uniform with increasing air flow rate, the temperature and gas species distributions lack uniformity due to different degrees of reactions across the bed. An increase in the air flow rate also results in a decrease in the gas residence time, thereby lowering the biomass conversion efficiency in the bed. At the optimum gas residence time, the concentration of hydrogen is maximum, while the concentrations of carbon dioxide and water vapor are minimum in the product gas. For efficient biomass gasification in a bubbling bed, the superficial gas velocity, u 0 , and average bubble diameter, D b , over the bed are related by gD b /u 0 = 3.0, where g = 9.81 m/s 2 is the gravity constant. This proposed model can therefore be used to size BFB reactors or set the operating gas velocity to achieve optimum gasification.

Optimization of Yield and Conversion Rates in Methane Dry Reforming Using Artificial Neural Networks and the Multiobjective Genetic Algorithm

“…The attributes of the MP-PIC approach have recently gained considerable interest in various processes, as it has been validated with experimental data, empirical correlations, and industrial applications. − Lu et al employed both the two-fluid model (TFM) and the MP-PIC approach for simulating the carbon dioxide photoreduction in annular CFB by varying the initial catalyst bed height and inlet gas velocity, among other parameters, for envisioning the rise in bed height and size of formed bubbles and determined that the MP-PIC approach surpassed the TFM for the motion of the catalyst within a single cell . Jaiswal et al used an MP-PIC approach to study the properties of bubbles within a CFB hosting a biomass gasifier process while simultaneously altering superficial gas velocities and char loads to identify the optimum gas residence time that corresponds with the peak yield of hydrogen . The primary equations governed by the MP-PIC method for gaseous and solid phases are listed in the Supporting Information.…”