Gas-solid Fluidization Research Articles

Investigation on the influence of particle size and porosity on solid phase fluidization and heat transfer in carbon fibre porous media

AbstractIn this paper, the 3D pore structure was reconstructed and solid phase fluidization in porous media was investigated. Based on the two fluid model, a computational fluid dynamics (CFD) model of gas–solid fluidization was established and the influence of particle size and porosity were investigated. When porosity was constant, during the fluidization process, the particle velocity and solid concentration standard deviation gradually decreased, and the bed height increased. At the same time, the smaller the particle size was, the smaller the solid concentration standard deviation was, the faster the bed height increased, and the more the particle temperature decreased. Based on the fixed value of particle size, when studying the effect of porosity on fluidization, it was found that with the enhancement of solid‐phase fluidization, particle velocity, solid concentration standard deviation, and particle temperature decreased, and bed height increased. Moreover, when porosity was large, particle velocity decreased rapidly, and solid concentration standard deviation reached a smaller minimum value, which took a longer time. Within the same time, the particle temperature also decreased less.

Investigation on NO and N2O emissions characteristics in deep peak regulation circulating fluidized bed boilers

Abstract To adapt to the increasing proportion of new energy power generation capacity, coal power must transition from its traditional role as the primary power source to serving as a fundamental backup and system regulation energy source. The circulating fluidized bed technology is known for its wide range of load regulation capabilities; however, emissions of pollutants during load regulation can exhibit significant variability. This study utilized Aspen Plus software to develop a circulating fluidized bed combustion model based on a gas-solid fluid dynamics model, an equivalent coal pyrolysis model, and a multi-phase macroscopic combustion reaction dynamics model of pyrolysis products. This model was used to predict both the temperature distribution within the furnace chamber and the distribution of pollutant concentrations. Predictions of pollutant emissions from 100 % load to 30 % load of the circulating fluidized bed were explored under the original combustion condition and 5 % proportion of recirculated flue gas. Under the primary combustion condition, the emission concentration of NO x showed a decreasing and then increasing trend with decreasing load, while the concentration of nitrous oxide, in contrast to NO x , showed an increasing and then decreasing trend. The effect of recirculated flue gas on pollutant emissions was not significant at reduced loads. This study aims to provide technical support and theoretical guidance for the management of pollutant emissions from the deep peak regulation of actual circulating fluidized beds.

Machine learning analysis of pressure fluctuations in a gas-solid fluidized bed

Pressure fluctuations in a gas-solid fluidized bed involve much information of the dynamic system. To uncover the value and significance of the pressure fluctuations time series, two meaningful tasks, i.e., the fluidization regime classification and the future state prediction, are investigated using Machine Learning (ML) algorithms, including Back Propagation neural network (BP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), Radial Basis Function network (RBF), Random Forest (RF) and Support Vector Machine (SVM). Findings indicate that the six ML methods rank their performance for the classification task from high to low as: BP ∼ RF > SVM > CNN ∼ LSTM ∼ RBF. For the one-step-ahead future state prediction, the accuracy goes from high to low as: BP ∼ RBF > CNN ∼ LSTM > SVM ∼ RF, whilst for the multistep-ahead prediction the LSTM approach shows the best performance. Additionally, the sampling frequency of pressure time series has significant effects on both tasks. Overall, this study demonstrates the capability of machine learning methods for the value analysis of nonlinear time series and the better operation of gas-solid fluidization systems.

Experimental and numerical investigation on the effect of surface roughness on the drag coefficient of a spherical particle

The drag coefficient (CD) for particles has a significant effect on the gas–solid fluidization characteristics. However, it is unclear whether the particle surface roughness (Rap) can notably affect CD at relatively low particle Reynolds numbers (Rep). Herein, the CD for several single near-spherical particles with different Rap and density were determined using high-speed videography in conjunction with the image analysis method. In addition, a quasi-direct numerical simulation (q-DNS) method was applied to further resolve the flow field surrounding the particle. Both experimental and simulation results suggest that within a low Rep range (<500), there is no evident difference in CD. However, if the Rep exceeds 500, CD gradually decreases with the increase in Rap. Since the separation point of the particle boundary layer moves downstream, the rough sphere experiences lower pressure in the wake vortex region. This study provides a valuable conclusion: the Rap is a non-negligible factor in studying hydrodynamic behaviors of two-phase flow, especially at high fluidization velocity.

Research on the generalization issue of the heterogeneous QC-EMMS drag model for gas-solid fluidization

In the circulating fluidized bed (CFB), the non-uniform mesoscale structure formed by the particle clustering effect causes a substantial decrease in the gas-solid drag. Since the degree of particle clustering varies with operating conditions, an accurate drag model needs to be universal in different heterogeneous flow conditions. In this study, the relationships between the Ψ factor in clusters' solid holdup model that characterizes the flow non-uniformity and the operating parameters of CFB (including slip velocity Reynolds number, Re*, bed-averaged solid volume fraction, εs,bed, and solid mass circulation rate, Gs) are established. It improves the adaptability of the QC-EMMS drag model under different working conditions. In previous research, we established two types of models that related Ψ factor to Re* and εs,bed respectively. However, there exists a problem of high dispersion of points, indicating that the selected parameters cannot fully describe the flow non-uniformity. Therefore, Gs is reintroduced to modify the two types of models. The results show that the prediction accuracy of the modified models is improved and the relative error is <10%, indicating that the non-uniform factor Ψ has a strong correlation with Gs. In addition, the quantitative relation between Re*, εs,bed, and Gs is derived from modified models, and the trend of relation is highly consistent with the fluidization diagram proposed by Yerushalmi J., which verifies the accuracy of modified models. Finally, numerical simulation of typical CFB cases proves the adaptability of the modified models in wide operating conditions of fluidization.

Machine Learning Assisted Experimental Characterization of Bubble Dynamics in Gas-Solid Fluidized Beds.

This study introduces a machine learning (ML)-assisted image segmentation method for automatic bubble identification in gas-solid quasi-2D fluidized beds, offering enhanced accuracy in bubble recognition. Binary images are segmented by the ML method, and an in-house Lagrangian tracking technique is developed to track bubble evolution. The ML-assisted segmentation method requires few training data, achieves an accuracy of 98.75%, and allows for filtering out common sources of uncertainty in hydrodynamics, such as varying illumination conditions and out-of-focus regions, thus providing an efficient tool to study bubbling in a standard, consistent, and repeatable manner. In this work, the ML-assisted methodology is tested in a particularly challenging case: structured oscillating fluidized beds, where the spatial and time evolution of the bubble position, velocity, and shape are characteristics of the nucleation-propagation-rupture cycle. The new method is validated across various operational conditions and particle sizes, demonstrating versatility and effectiveness. It shows the ability to capture challenging bubbling dynamics and subtle changes in velocity and size distributions observed in beds of varying particle size. New characteristic features of oscillating beds are identified, including the effect of frequency and particle size on the bubble morphology, aspect, and shape factors and their relationship with the stability of the flow, quantified through the rate of coalescence and splitting events. This type of combination of classic analysis with the application of the ML assisted techniques provides a powerful tool to improve standardization and address the reproducibility of hydrodynamic studies, with the potential to be extended from gas-solid fluidization to other multiphase flow systems.

Unified gas-kinetic wave–particle method for polydisperse gas–solid particle multiphase flow

The gas-particle flow with multiple dispersed solid phases is associated with a complicated multiphase flow dynamics. In this paper, a unified algorithm is proposed for the gas-particle multiphase flow. The gas-kinetic scheme (GKS) is used to simulate the gas phase and the multiscale unified gas-kinetic wave–particle (UGKWP) method is developed for the multiple dispersed solid particle phase. For each disperse solid particle phase, the decomposition of deterministic wave and statistic particle in UGKWP is based on the local cell's Knudsen number. The method for solid particle phase can become the Eulerian fluid approach at the small cell's Knudsen number and the Lagrangian particle approach at the large cell's Knudsen number. This becomes an optimized algorithm for simulating dispersed particle phases with a large variation of Knudsen numbers due to different physical properties of the individual particle phase, such as the particle diameter, material density, etc. The GKS-UGKWP method for gas-particle flow unifies the Eulerian–Eulerian and Eulerian–Lagrangian methods. The particle and wave decompositions for the solid particle phase and their coupled evolution in UGKWP come from the consideration to balance the physical accuracy and numerical efficiency. Two cases of a gas–solid fluidization system, i.e. one circulating fluidized bed and one turbulent fluidized bed, are simulated. The typical flow structures of the fluidized particles are captured, and the time-averaged variables of the flow field agree well with the experimental measurements. In addition, the shock particle–bed interaction is studied by the proposed method, which validates the algorithm for the polydisperse gas-particle system in the highly compressible case, where the dynamic evolution process of the particle cloud is investigated.

Evaluation of array capacitive sensor for local concentration measurement of gas–solid particles flow by coupled fields based on CFD-DEM

An array capacitive sensor adopting two excitation modes is designed for measuring local particle concentration in gas–solid two-phase flow. Considering the difficulty in obtaining instantaneous flow parameters under dynamic experiments, a 3D dynamic simulation model coupling gas–solid two-phase fluid and electrostatic fields is proposed to quantitatively evaluate the sensor performance. In the coupling model, Computational Fluid Dynamics and Discrete Element Method are applied to establish two-phase fluid field for particle micro-distribution. The method of defining particle shape and position is used to set permittivity distribution in electrostatic field for coupling gas–solid fluid field and electrostatic field of array capacitive sensor. Using the method of reconstructed image and data fitting, the performance of two excitation modes are evaluated under a typical dynamic flow with structure of stratified flow. Experimental result validates the simulation conclusions and proves the effectiveness of the coupling simulation model for evaluating capacitive sensors in gas–solid flow systems.

Effect of fluidized bed shapes on gas-solid fluidization characters and flow regime transition

The technology of gas-solid dense-phase fluidized bed separation is applied in the separation of fine particles in minerals due to its stable and reliable characteristics. The experiment collected time-domain distribution signals of pressure drop from fluidized beds with different shapes, systematically analyzed the spatiotemporal distribution characteristics of bed density, and studied the effects of bed shape and fluidization number N on the fluidization and flow transition of the bed layer. The results indicate significant differences in Umf among fluidized beds with different shapes, and the tapered bed requires the highest energy for fluidization. When N=1.3, the cylindrical bed layer is the most stable, maintaining an average density of 1.8 g/cm3, with a density fluctuation variance of 0.02 g/cm3. Additionally, when N<1.0, almost no bubbles inside the bed layer. With an increase in gas velocity, the time-domain distribution of pressure drop signals shows different degrees of fluctuation. When N=1.1, Conspicuous non-uniform particle flow is evident in the tapered bed, with a bubble main frequency of 8 Hz, an amplitude of 27.1, and the highest pressure drop energy signal, indicating that the tapered bed enters the bubbling state prematurely. Finally, fine coal separation experiments were conducted, and the results showed that the minimum E value for the cylindrical bed is 0.08 g/cm3, while the maximum E value for the tapered bed is 0.17 g/cm3.

Fast simulation of industrial-scale bubbling fluidized beds using mesoscience-based structural model

Fast and accurate simulation of industrial-scale reactors is the first and critical step towards virtual process engineering. In this article, we show for the first time that a recently proposed mesoscience-based structural model, which treats the bubble phase and the emulsion phase as the two interpenetrating continua, is able to achieve fast and accurate simulation of the hydrodynamics of four industrial-scale bubbling fluidized beds, in terms of macroscopic distribution of parameters such as the bed expansion rate, the axial and radial profiles of solid concentration and velocity. On the other hand, due to the fact that the mesoscale bubbling structures have been averaged over during the model derivation and the mesh sizes used are comparative to or larger than the bubble diameter, it is impossible to correctly capture the evolution, coalescence and breakup of bubbles. Therefore, the mesoscience-based structural model is best used as a computational fluid dynamics based method for fast engineering simulations of gas-solid fluidization.

Multi-scale simulation of particle density effects on hydrodynamics in dense gas-solid fluidized beds

Gas-solid fluidization has gained prominence in chemical engineering, particularly for processes involving high-density particles. However, a notable dearth of research on the fundamental fluidization of high-density particles exists, distinct from the well-studied low-density particles. This knowledge gap presents significant challenges in designing and operating fluidized beds for such processes. In this study, we employ the CPFD scheme to comprehensively investigate how particle density affects hydrodynamics. Simulation results consistently align with experiments for pressure drop and minimum fluidization velocity, underscoring the model reliability. Importantly, typical empirical correlations for predicting minimum fluidization velocity display substantial inaccuracies, particularly for high-density particles. Additionally, we identify marked disparities in fundamental fluidization behavior between high and low-density particles, with increasing density leading to deteriorating fluidization performance, characterized by larger bubbles, defluidized regions, gas streaming, and reduced bed expansion ratios. Investigating high-density particle gas-solid fluidization systems promises valuable theoretical insights, bridging the gap between theory and practice.

Segregation of binary particles in pulsed gas-solid fluidized bed

Pulsating airflow-driven gas-solid fluidization is an effective method to segregate particles of different densities. The current article presents an elaborate computational study on the development of simulation methodology, and parametric trend identification of pulsating airflow-driven granular segregation for the first time using computational fluid dynamics (CFD), discrete element modeling (DEM) coupled analysis. The rigorous validation of the computational prediction has been produced against the experimental result reported by Li et al. (2021) and an excellent agreement has been observed. Segregation behavior for different density ratios of particles, different patterns of pulsation, and different particle shapes is presented in the current article. The segregation index improves with the density ratio of the binary mixture of particles, but the rate of increment of the segregation index is highly dependent on the airflow rate. Near the optimal value of airflow rate, the rate of increment of the segregation index reduces with the density ratio. Keeping the bed height and material property constant, different patterns of pulsation namely—sinusoidal, triangular, and exponential have been adopted. The segregation index is higher for sinusoidal patterns, whereas it reduces when the pattern is changed to triangular and exponential. Particles of constant volume with different shapes are also considered to understand the effect of particle shape on segregation. The segregation index deteriorates with the increasing surface area of the particle at constant volume.

Dynamic energy-minimization multi-scale heterogeneous continuum evolution model for gas-solid fluidization

Traditional gas–solid continuum model is still computationally inaccurate and physically unreasonable, due to a homogeneous assumption that particles are uniformly dispersed in a computational grid. Therefore, its application to simulate industrial gas–solid fluidization needs further improvement. Here, a novel heterogeneous continuum model, named dynamic energy-minimization multi-scale (EMMS) evolution model, was developed for accurate and fast simulations of gas–solid fluidization systems. In this model, the evolution of gas–solid heterogeneous multiscale structure in each computational grid was directly resolved by EMMS model, which was then coupled with multiphase mixture model to dynamically calculate phase holdup and flow field. Both S-shaped axial voidage profiles and spatial distribution of solid phase with the presence of a core-annulus structure were successfully captured. Compared with two-fluid model, dynamic EMMS evolution model showed great advantages in various aspects, including (a) model accuracy: predicted “S” type distribution of bed voidage was closer to experimental data; (b) stability: a rather larger time step of 0.1 s could be used; and (c) computational cost: dynamic EMMS evolution model was 245 times faster than two-fluid model. Based on dynamic EMMS evolution model, the complex multiscale hydrodynamics can be well simulated and understood, and will help the design and scale-up of large-scale gas–solid fluidized bed reactors.

Simulation of heavy particles fluidized with non-Newtonian fluids

Aqueous solutions of very fine particles behave as non-Newtonian fluids and are used to fluidize heavy spherical particles creating large bed expansions and inhomogeneities similar to those observed in traditional fluidization of particles with Newtonian fluids. This study attempts to extend the validity of traditional computational fluid dynamics (CFD) models that are routinely used for gas-solids fluidization to non-Newtonian fluids. The modified model incorporates a shear-thinning power law viscosity for the non-Newtonian fluid and a recently published drag correlation derived from direct numerical simulation data of flow around randomly positioned spheres. This validation study compares numerical results obtained in 3D fluidized beds with two independent experimental bed porosity data sets for heavy steel and glass spherical particles. The current CFD results show a better agreement with experimental data obtained over a wide range of porosity and Reynolds number than a previously published semi-empirical correlation.

Comparison and validation of various drag models for fluidization characteristics of bubble fluidized beds with a high-speed particle image velocimetry experiment

Bubbling liquefaction of dense particles is one of the most common forms of industrial fluidization in gas–solid flow systems. Computational fluid dynamics and the discrete element method are important tools for studying dense gas–solid flows. In these methods, the momentum transfer between phases relies on a drag model, so a reasonable choice of drag model is crucial for accurately predicting the hydrodynamic behavior of dense gas–solid flows. This paper investigates the effect of different drag models on the flow behavior prediction of dense gas–solid flow for the “Small-Scale Challenge Problem-I” published by the National Energy Technology Laboratory in 2013. The gas–solid fluidization characteristics, such as instantaneous particle flow processes, particle velocity vector distributions, changes in the fluidized bed height, and average gas phase pressure drops, were compared for different drag models. A detailed validation analysis of each dominant drag model was carried out in conjunction with the experimental data. The results show that the drag model significantly affects the numerically predicted results of particles’ hydrodynamic behavior, especially in terms of the bed height variation and the remixing behavior of particles. These research results are expected to improve the predictive accuracy of gas–solid flow hydrodynamic behavior and provide guidance for designing and optimizing fluidized beds, which has theoretical and practical significance.

Gas–solid reactor optimization based on EMMS-DPM simulation and machine learning

Design, scaling-up, and optimization of industrial reactors mainly depend on step-by-step experiments and engineering experience, which is usually time-consuming, high cost, and high risk. Although numerical simulation can reproduce high resolution details of hydrodynamics, thermal transfer, and reaction process in reactors, it is still challenging for industrial reactors due to huge computational cost. In this study, by combining the numerical simulation and artificial intelligence (AI) technology of machine learning (ML), a method is proposed to efficiently predict and optimize the performance of industrial reactors. A gas–solid fluidization reactor for the methanol to olefins process is taken as an example. 1500 cases under different conditions are simulated by the coarse-grain discrete particle method based on the Energy-Minimization Multi-Scale model, and thus, the reactor performance data set is constructed. To develop an efficient reactor performance prediction model influenced by multiple factors, the ML method is established including the ensemble learning strategy and automatic hyperparameter optimization technique, which has better performance than the methods based on the artificial neural network. Furthermore, the operating conditions for highest yield of ethylene and propylene or lowest pressure drop are searched with the particle swarm optimization algorithm due to its strength to solve non-linear optimization problems. Results show that decreasing the methanol inflow rate and increasing the catalyst inventory can maximize the yield, while decreasing methanol the inflow rate and reducing the catalyst inventory can minimize the pressure drop. The two objectives are thus conflicting, and the practical operations need to be compromised under different circumstance.

Energy budget of cold and hot gas–solid fluidized beds through computational fluid dynamics-discrete element method simulations

Direct energy budget is carried out for both cold and hot flow in gas–solid fluidization systems. First, the energy paths are proposed from thermodynamic viewpoints. Energy consumption means total power input to the specific system, and it can be decomposed into energy retention and energy dissipation. Energy retention is the variation of accumulated mechanical energy in the system, and energy dissipation is the energy converted to heat by irreversible processes. Then based on the computational fluid dynamics-discrete element method framework, different energy terms are quantified from the specific flow elements of fluid cells and particles as well as their interactions with the wall. In order to clarify the energy budget, it is important to identify which system is studied: the particle-fluid system or the particle sub-system. For the cold flow, the total energy consumption of the particle sub-system can well indicate the onset of bubbling and turbulent, while the variation of local energy consumption terms can reflect the evolution of heterogeneous structures. For the hot flow, different heat transfer mechanisms are analyzed and the solver is modified to reproduce the experimental results. The impact of the heat transfer mechanisms and heat production on energy consumption is also investigated. The proposed budget method has proven to be energy-conservative and easy to conduct, and it is hopeful to be applied to other multiphase flow systems.

Comparative analysis of particle density effects on initial fluidization in gas-solid fluidized beds

Gas-solids fluidization technology is commonly used in chemical engineering processes involving high-density particles, often encountering poor defluidization phenomena. However, there is limited research examining the applicability of flow characteristics and empirical correlations established for low-density, easily fluidizable particles in guiding the flow behavior of high-density particles. This knowledge gap poses significant challenges for designing and operating fluidized beds in relevant processes involving high-density particles. This study comprehensively evaluates the effect of particle density on hydrodynamics during the initial fluidization stage using experimental, empirical correlations and simulation methods. Results of preliminary experiments indicate that empirical correlations for predicting Umf may exhibit significant errors of high-density particles. However, the experiments face challenges in eliminating the influence of different particle size distribution. In contrast, the simulation results present a high consistency with the experiments after sensitivity analysis. Further simulation results illustrate that empirical correlations for Umf may only yield relatively accurate predictions within the narrow density range. As the particle density increases, the predicted errors tend to increase. During the initial fluidization stage, three distinct fluidization stages can be observed, as revealed by both experiments and simulations. These stages can be attributed to the practical particle size distribution. Furthermore, it is noted that as the particle density increases, the fluidization performance tends to deteriorate. This study endeavors to advance the understanding of the effect of particle density on hydrodynamics during the initial fluidization stage. Nonetheless, more studies are still needed to enhance and deepen the current knowledge of the fluidization of high-density particles.

In-Line Detection of Bed Fluidity in Gas-Solid Fluidized Beds Using Near-Infrared Spectroscopy.

A novel approach was developed to detect bed fluidity in gas-solid fluidized beds using diffuse reflectance near-infrared (NIR) spectroscopy. Because the flow dynamics of gas and solid phases are closely associated with the fluidization state, the fluidization quality can be evaluated through hydrodynamic characterization. In this study, the baseline level of NIR spectra was used to quantify the voidage of the fluidized bed. Two indicators derived from the NIR baseline fluctuation profiles were investigated to characterize bed fluidity, named bubble proportion and skewness. To establish a robust fluidity evaluation method, the relationships between the indicators and bed fluidity were investigated under different conditions firstly, including static bed height and average particle size. Then, a generalized threshold was identified to distinguish poor and good bed fluidity, ensuring that the probability of the α- and β-errors was less than 15% regardless of material conditions. The results show that both indicators were sensitive to changes in bed fluidity under the investigated conditions. The indicator of skewness was qualified to detect bed fluidity under varied conditions with a robust threshold of 1.20. Furthermore, the developed NIR method was successfully applied to monitor bed fluidity and for early warning of defluidization in a laboratory-scale fluidized bed granulation process.

Comparative Analysis of Combustion Characteristics of a CFB Boiler during the Changes Process between High-Rated Loads and Low-Rated Loads

In order to alleviate problems such as large fluctuations in grid load caused by the high proportion of renewable energy, circulating fluidized bed (CFB) power plants undertake the task of rated load regulation. This study discussed the combustion characteristics of a 100 KW CFB boiler during the operation process of varying loads and analyzes the combustion characteristics, load regulation rate and emissions variation law during the operation process of high- and low-rated load intervals. The experimental results showed that under the condition of a high-rated load, the average temperature of each area in the furnace was proportional to the size of the load. Under low-rated load conditions, the temperature change increased first and then decreased with the reduction in load. In the 30% load stage, the lowest temperature in the riser was 740 °C, while the temperature in the loop seal was even as low as 650 °C. The concentrations of O2, CO and unburned carbon mainly depended on the combustion reaction intensity under each load condition, which showed a higher trend at low load (30%). In terms of NOx emission, it was proportional to the load in the high-rated load range. However, the NOx generation at the 30% load was about 30 mg/Nm3(@6%O2) higher than the 50% load. In addition, the regulation load rate (2.5%/min) between high-load conditions exhibited significantly greater than that between low-load conditions (0.78%/min). Therefore, the low-load operation will face problems such as low furnace temperature, uneven gas–solid fluidization, and difficult control of pollutant generation, which need to be paid attention to during operation.