Simulation of gas–solid flow characteristics in a circulating fluidized bed based on a computational particle fluid dynamics model

Simulation and Optimization of a Multistage Interconnected Fluidized Bed Reactor for Coal Chemical Looping Combustion

This work established a three-dimensional model of a chemical looping system with multistage reactors coupled with hydrodynamics and chemical reactions. The thermal characteristics in the chemical looping combustion (CLC) system were simulated using coal as fuel and hematite as an oxygen carrier (OC). Some significant aspects, including gas composition, particle residence time, backmixing rate, wall erosion, carbon capture rate, etc., were investigated in the simulation. Owing to the optimization by adding baffles in the fuel reactor (FR), the gas conversion capacity of the multistage FR was high, where the outlet CO₂ concentration was as high as 93.8% and the oxygen demand was as low as 3.8%. Through tracing and analyzing the path of char particles, we found that the residence time of most char particles was too short to be fully gasified. The residual char will be entrained into the air reactor (AR), reducing the CO₂ capture rate, which is only 80.3%. In the simulation, the wall erosion on the cyclone could be relieved by increasing the height of the horizontal pipe. In addition, improving the structure of the AR loop seal could control the residual char entrained by OC particles to the AR, and the CO₂ capture rate was increased up to 90% in the multistage CLC reactor.

A Deep Learning-Based Parameter Prediction Method for Coal Slime Blending Circulating Fluidized Bed Units

Coal slime blending can effectively improve the utilization rate of fossil fuels and reduce environmental pollution. However, the combustion in the furnace is unstable due to the empty pump phenomenon during the coal slurry transport. The combustion instability affects the material distribution in the furnace and harms the unit operation. The bed pressure in the circulating fluidized bed unit reflects the amount of material in the furnace. An accurate bed pressure prediction model can reflect the future material quantity in the furnace, which helps adjust the operation of the unit in a timely fashion. Thus, a deep learning-based prediction method for bed pressure is proposed in this paper. The Pearson correlation coefficient with time correction was used to screen the input variables. The Gaussian convolution kernels were used to implement the extraction of inertial delay characteristics of the data. Based on the computational theory of the temporal attention layer, the model was trained using the segmented approach. Ablation experiments verified the innovations of the proposed method. Compared with other models, the mean absolute error of the proposed model reached 0.0443 kPa, 0.0931 kPa, and 0.0345 kPa for the three data sets, respectively, which are better than those of the other models.

Simulation of a Scaled down 250 MWe CFB Boiler Using Computational Particle Fluid Dynamics Numerical Model

Eulerian-Eulerian approach and conventional Eulerian-Lagrangian model are computationally exhaustive for modelling circulating fluidized bed (CFB) riser which has wide particle size distribution and billions of particles Alternatively, the relatively recent Eulerian- Lagrangian computational particle fluid dynamics (CPFD) model enables simulation of the CFB system with lesser computational resources. Most of the published studies on CPFD simulations of CFB risers deal with single grate system. The present study aimed to investigate the performance of the CPFD model for predicting solids distribution in a CFB riser with pant-leg structure (dual grate) and characteristics similar to a commercial boiler. Experiments conducted in a scaled down 250 MWe CFB facility according to Glicksman’s simplified similarity laws for fluidized beds were simulated using commercial code Barracuda. The bottom dense bed, upper lean solid phase, increase in bottom bed voidage with increasing fluidizing velocity and reducing solids inventory, decrease in bottom bed solids concentration with decrease in particle size and exchange of solids between the legs typically occurring in a CFB with pant-leg structure were successfully captured by the CPFD calculations. Simulation results showed that the upper solids concentration is hardly influenced by the solids inventory level in line with the experimental observation, therefore the amount of solids inventory can be optimized during actual operation. The predicted pressures varied from the average experimental pressure data within the range –10 to 39 %.