Gas-solid Fluidized Bed Research Articles

Fluidization of fine coal particles in an ultrasound enhanced gas-solid fluidized bed

Fluidization of fine coal particles in an ultrasound enhanced gas-solid fluidized bed

Experimental quantification of interparticle forces in gas-solid fluidized beds operating at temperatures from ambient to 1500 °C

The successful design and operation of high-temperature gas-solid fluidized bed reactors require a deep understanding of interparticle forces (IPFs). However, experimentally quantifying IPFs at elevated temperatures has been a significant challenge due to the lack of suitable methods. This study addresses this gap by introducing a simple yet reliable experimental approach to quantify IPFs in a gas-solid fluidized bed across a temperature range from ambient to 1500 °C. The experimental results reveal that IPFs increase gradually with temperatures up to 1200 °C and become more pronounced at higher temperatures. Smaller particles, or those prone to changes in morphological, structural, and chemical properties—such as softening, sintering, or the formation of low-melting-point eutectic compounds at high temperatures—intensify IPFs significantly. This phenomenon is corroborated by our experiments and comparison with literature data across various temperatures and particle types. Finally, two empirical correlations are proposed to predict IPFs as temperature and particle diameter functions for coarse particles in high-temperature fluidized beds. These findings enhance the understanding of IPFs in high-temperature fluidized beds and are valuable for developing such systems for industrial applications.

Deep learning assisted characterization of bubble behavior in a gas-solid fluidized bed with binary particle mixtures

Gas-solid fluidized beds are widely applied as chemical reactors, and the fluidization quality in the bed is closely related to bubble behavior. Digital image processing is a commonly used non-invasive method for bubble behavior analysis, but it is usually constrained by experimental conditions such as lighting, making identification of bubble and emulsion phases still challenging. Herein, deep learning is applied in this study to optimize traditional digital image processing techniques. By evaluating different deep learning models (FCN, DeepLab V3, U-Net), rapid and accurate identification and segmentation of bubble images can be achieved, and the U-Net model performs best, achieving an identification accuracy of 99.05 %. Further application of U-Net to analyze bubble behavior demonstrates that deep learning methods enable efficient and accurate identification of bubbles and real-time analysis of bubble behavior, highlighting the significant potential application of deep learning in the field of complex hydrodynamics in fluidized beds.

Acoustic emission and machine learning algorithms for particle size analysis in gas-solid fluidized bed reactors

In this work, a combination of an acoustic emission (AE) technique and a machine learning (ML) algorithm (Random Forest (RF) and Gradient Boosting Regressor (GBR)) is developed to characterize the particle size distribution in gas-solid fluidized bed reactors. A theoretical approach to explain the generation of acoustic emission signal in gas-solid flows is presented. An AE signal is generated in gas-solid fluidized beds due to the collision and friction between fluidized particles as well as between particles and the bed inner wall. The generated AE signal is in the form of an elastic wave with frequencies >100 KHz and it propagates through the gas-solid mixture. An inversion algorithm is used to extract the information about the particle size starting from the energy of the AE signal. The advantages of this AE technique are that it is a cheap, sensitive, non-intrusive, radiation-free, suitable for on-line measurements. Combining this AE technique with ML algorithms is beneficial for applications to industrial settings, reducing the cost of signal post-processing. Experiments were conducted in a pseudo-2D flat fluidized bed with four glass bead samples, with sizes ranging from 100 μm to 710 μm. AE signals were recorded with a sampling frequency of 5 MHz. The AE signal post-processing and data preparation for the ML process are explained. For the ML process, the AE frequency, AE energy and particle collision velocity data sets were divided into training (60%), cross-validation (20%) and test sets (20%). Two ensemble ML approaches, namely Random Forest and Gradient Boosting Regressor, are applied to predict particle sizes based on the AE signal features. The combination of these two models results in a coefficient of determination (R2) value greater than 0.9504.

Numerical Investigation into Gas-Solid Fluidized Bed Reactors for Activating the Iron-based Fischer-Tropsch Synthesis Catalyst

Abstract The present work conducted experimental and computational fluid dynamics simulation studies on the hydrodynamic characteristics of gas-solid fluidized bed reactors for activating the iron-based Fischer-Tropsch synthesis catalyst. In order to provide reliable guidance for the design and scale-up of the reactor, the present study applied experimental data from a pilot fluidized bed device to verify the accuracy of the drag force model used in the two-fluid model and further used the validated drag force model to simulate an industrial-scale fluidized bed activation reactor. It was found that the bubbling-EMMS drag model can accurately simulate the pilot-scale fluidized bed in the flow regimes ranging from bubbling to turbulent fluidization. Simulation studies on the industrial-scale fluidized bed activation reactor showed that the reactor was in a turbulent fluidization regime under the designed operating gas velocity, with high gas-solid dispersion efficiency, reasonable utilization of reactor space, and low gas-solid separation load.

Fluidization behavior of stirred gas–solid fluidized beds: A combined X-ray and CFD–DEM–IBM study

Stirred gas–solid fluidized bed reactors are commercially employed in polyolefin manufacturing, but the complex gas–solid contacting dynamics pose challenges in design, scale-up, and operation. In this study, the influence of agitation on the fluidization performance of Geldart B particles was investigated experimentally by X-ray imaging and pressure drop measurements and numerically by Computational Fluid Dynamics (CFD) - Discrete Element Method (DEM) - Immersed Boundary Method (IBM). The experimentally obtained minimum fluidization curve and time-averaged pressure drop show good qualitative agreement with the simulation results. Visual observations underscore that an increase in the angular velocity of the agitator results in reduced bubble size and improved bed homogeneity, as further evidenced by reduced pressure fluctuations. Furthermore, the simulations reveal that while the impeller enhances solids agitation, a proper design study is imperative, considering that static immersed bodies, such as the stirrer shaft, can adversely impact solids motion.

Heat transfer efficiency in gas–solid fluidized beds with flat and corrugated walls

Abstract Gas–solid fluidized bed reactors exhibit improved heat and mass transfer performance as compared to packed beds. Corrugated walls installed in narrow gas–solid bubbling fluidized bed (CWBFB) enclosures have been observed to decrease minimum bubbling velocity, reduce bubble size, improve gas distribution, provide stable operation, and minimize particle carryover or loss. Thorough analyses of the wall-to-bed heat transfer coefficient in flat- (FWBFB) and corrugated- (CWBFB) wall bubbling fluidized beds have been performed for a variety of operating conditions and geometric parameters. Fast-response self-adhesive heat flux probes and thermocouples were used to simultaneously measure the wall-to-bed heat flux, surface and bed temperatures, and were used to determine the heat transfer coefficient (HTC) at various axial and lateral locations. For a given set of parameters, a significant increase in HTC was observed at lower gas flow rates in CWBFB as compared to FWBFB. It was shown that CWBFB inventory required lower U mb (gas flow rate) as compared to FWBFB. Full 3-D transient Euler–Euler CFD simulations using the kinetic theory of granular flow were also performed, which confirmed the experimental results.

CatchyCFDEM: Open-source microkinetic modeling of heterogeneous catalysis in Eulerian-Lagrangian CFD applied to oxidative coupling of methane

This work introduces catchyCFDEM, an OpenFOAM-based open-source CFD-DEM framework designed for simulating reactive gas–solid fluidized beds with both gas-phase chemistry and heterogeneous catalytic chemistry. A convective gas–solid heat transfer model is implemented and validated using experimental data gathered in a non-reactive pseudo-2D fluidized bed. Catalytic chemistry including different levels of complexity are included: pseudo-homogeneous, heterogeneous without mass transfer, and heterogeneous with diffusive mass transfer. Verification studies are performed by simulating a packed bed reactor using catchyCFDEM and comparing mass fraction profiles to a 1D plug flow simulation in Cantera. To enhance the computational efficiency of the resource-intensive reactive CFD-DEM simulations, speed-up algorithms such as Particle Agglomeration (PA) and in-situ adaptive tabulation (ISAT) are integrated in catchyCFDEM. As a case study, a gas–solid vortex reactor (GSVR) is simulated using the validated framework. The applicability of the GSVR for the oxidative coupling of methane (OCM) is evaluated based on an isothermal analysis evaluating the influence of several operational and geometrical parameters on the process. Finally, a proof-of-concept adiabatic simulation of the GSVR is presented. The catchyCFDEM framework is made publicly available on GitHub.

A sub-grid gas–solid interaction model for coarse-grained CFD–DEM simulations

Computational fluid dynamics has been coupled with coarse-grained discrete element method (CFD–CGDEM) to study industrial-scale gas–solid fluidized beds. However, solving the fluid flow at a grid size of several coarse-grained particles (CGPs) cannot accurately predict the gas–solid interaction, and thus reasonable correction model is required. This study develops a sub-grid model to correct the gas–solid interaction and thereby improve the accuracy of coarse-grid CFD–CGDEM simulation. First, the fluid grids with the size of several CGPs are divided into the auxiliary three-dimensional sub-grids. Then, the CGPs are mapped onto the sub-grids by the weight function to reconstruct the local solid velocities and solid fractions of the sub-grids. After that, the simplified mass and momentum conservation equations are solved to resolve the local gas velocities of sub-grids. Finally, the drag on each CGP is corrected based on the local gas-phase hydrodynamics of sub-grids. Results show that the sub-grid model resolving the local gas velocity in all the three directions works better than that just in the main stream direction. The coarse-grid CFD–CGDEM simulations with this sub-grid model agree well with the experiment for the bubbling, turbulent and rapid fluidized beds. Besides, by solving the simplified conservation equations with graphics processing units, the coarse-grid CFD–CGDEM coupled with the sub-grid model achieves 674 times speedup compared to the fine-grid simulation, demonstrating a much more efficient method to study industrial-scale gas–solid fluidized beds.

Coke evolution study in a liquid atomization into gas-solid fluidized bed to convert fructose to value-added chemicals

Glucose and fructose are valuable compounds, which have the potential to displace fossil fuels as a feedstock for highly valued products. One of the promising reactions is the oxi-dehydration of fructose to produce Hydroxymethylfurfural, 2,5-diformylfuran, and furfural. Current processes to produce platform chemicals like Hydroxymethylfurfural, furfural, and 2,5-diformylfuran operate in liquid phase with homogeneous heterogeneous catalyst, but commercialization is limited by scale and environmental impact of the large volume solvent required. Here we developed a gas-phase process to convert fructose in a fluidized bed reactor over Mo − V − WO3/TiO2. However, in the catalytic oxi-dehydration of fructose, coke forms on active sites and decreases conversion but increases selectivity. Coke and reactor performance depends on fructose concentration and O2/fructose ratio. In the first 2 h, acidic amorphous coke accumulates (based on TPO, FTIR, and UV–vis). Feeding excess O2 maintains coke in an amorphous structure and liberates oxide sites. Whereas coke promotes, water block active site reducing yield. In this work, we optimized the operating parameters of fructose concentration (%) and O2/fructose ratio, while simultaneously mitigating the issues associated with coke formation and its detrimental effects.

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.

The transition to turbulent fluidization in a gas-solid fluidized bed operating from ambient temperature to 1600 °C

Turbulent fluidized bed proves effective in industrial processes due to superior heat and mass transfer and chemical reaction performance. However, understanding the transition to turbulent fluidization remains limited, especially at temperatures exceeding 1000 °C, making it challenging to develop high-temperature fluidized bed applications. This paper presents an experimental investigation on the turbulent fluidization onset velocity (Uc), measured in a 30 mm diameter bed using corundum particles with average diameters from 0.68 mm to 1.58 mm in temperatures from ambient to 1600 °C. Experimental results reveal that Uc increases with temperature up to 600 °C, stabilizes within the 600–1200 °C range, and then decreases above 1200 °C, demonstrating the varying relative significance of hydrodynamic and interparticle forces at different temperatures. To help design and operate high-temperature applications of turbulent fluidization, we developed Uc correlations based on experimental data from both literature sources and this study, covering temperatures of up to 1600 °C and particles of Groups A to D.

Separation density prediction of geldart A– dense medium in gas-solid fluidized bed coal beneficiators

Gas-solid Fluidized Bed Coal Beneficiator (GFBCB) process is a crucial dry coal beneficiation fluidization technology. The work employs the GFBCB process alongside a novel Geldart A– dense medium, consisting of Geldart A magnetite particles and Geldart C ultrafine coal, to separate small-size separated objects in the GFBCB. The effects of various operational conditions, including the volume fraction of ultrafine coal, the gas velocity, the separated objects size, and the separation time, were investigated on the GFBCB's separation performance. The results indicated that the probable error for 6∼3 mm separated objects could be controlled within 0.10 g/cm3. Compared to the traditional Geldart B/D dense medium, the Geldart A/A– dense medium exhibited better size-dependent separation performance with an overall probable error 0.04∼0.12 g/cm3. Moreover, it achieved a similar separation accuracy to the Geldart B/D dense medium fluidized bed with different external energy for the small-size object beneficiation. The work furthermore validated a separation density prediction model based on theoretical derivation, available for both Geldart B/D dense medium and Geldart A/A– dense medium at different operational conditions.

Utility of argon as a static charge and wall fouling suppressant in atmospheric gas-solid fluidized beds

This work aimed to study the usability of argon to mitigate electrostatic charging and fouling of polyethylene resin fluidized in a stainless-steel column. Nitrogen, argon, and their binary mixtures of various concentrations were utilized to fluidize polyethylene. Successive and intermittent fluidizations with argon and nitrogen were also carried out to explore the feasibility of using argon in commercial reactors. Fluidization with pure argon resulted in the lowest specific charge and substantially reduced the fouling by 90% relative to pure nitrogen. The results for the mixture trials showed a decreasing non-linear trend as the concentration of argon increased. Even presence of 10 vol% argon in the mixture reduced the wall fouling by 50%. Successive and intermittent fluidization also yielded fouling and specific charge values comparable to pure argon. The results are attributed to argon's relatively low dielectric strength and subsequent localized gas discharge within the particle-dense section of the bed.

A dual-grid approach to speed up large-scale CFD-DEM simulations

With the development of heterogeneous computing, the coupling of discrete element method (DEM) and computational fluid dynamics (CFD) has become an effective approach to simulate large-scale particle–fluid systems. With various models and algorithms accelerating the DEM part significantly, the CFD part becomes an efficiency bottleneck, especially for large-scale simulations. A dual-grid approach is proposed to address this issue. On the fine grid, the fluid velocity and particle–fluid interaction are solved without compromising accuracy in capturing flow details, and the pressure is solved on the coarse grid to ensure mass conservation to boost the CFD part. This approach is validated by simulating several gas–solid fluidized beds, demonstrating a performance speedup of 2.6 for low workload and 10.1 for high workload.

Segregation of binary particles in gas-solid fluidized bed

Segregation of binary particles in gas-solid fluidized bed

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.

A comparative assessment of CFD based LSTM and GRU for hydrodynamic predictions of gas-solid fluidized bed

Numerical simulation of large-scale fluidized beds presents significant challenges for both scaling up the simulations and evaluating their performance, as full-scale computational fluid dynamics (CFD) simulations incur substantial computational time and cost. To overcome these challenges, we propose a novel approach that combines CFD simulations with data-driven deep-learning models to predict complex hydrodynamics. In this study, we trained two machine learning (ML) algorithms, namely the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), using spatiotemporal data obtained from CFD simulations. Impressively, the trained models accurately predicted future events for successive time steps, exhibiting excellent agreement for mean voidage, gas velocity, and particle velocity. Among the models utilized, LSTM outperformed the other. This demonstrated its superior ability to capture spatiotemporal CFD data while requiring less computational time and power. The trained models provide a reliable means to extrapolate desirable mean particle velocity and voidage values within fluidized beds. These findings hold promising opportunities for enhancing the design and operation of gas-solid fluidized bed reactors, potentially leading to a significant reduction of up to 57–68% in simulation time.

Numerical simulations of CO2 absorption by MgO-based sorbent in a gas–solid fluidized bed

Given that carbon dioxide (CO2) constitutes the predominant proportion of greenhouse gas emissions, its capture, utilization and storage have always been a subject of great concern. Gas-solid fluidized bed has become one of the commonly applied equipment for the removal of CO2 from flue gases owing to its advantages such as exceptional mixing effect and substantial interfacial area between the phases. However, there is a lack of attention to the mechanism of CO2 absorption by individually-tracked moving solid sorbents as well as their effects on the decarbonization performance in a gas–solid fluidized bed. In this study, CO2 removal model by solid particles based on MgO adsorbent was established with the implementation of the shrinking-core model and each particle was tracked individually. Results validated the accuracy of the absorption model and revealed that smaller sizes of the solid absorbent, lower inlet gas velocities and larger inlet CO2 mass fractions exhibit relatively high CO2 removal efficiency. In addition, the decarbonization performance of fluidized and packed beds was compared. The packed bed exhibits more uniform gas flow and higher CO2 removal efficiency, whereas the fluidized bed provides larger interphase contact area, which facilitates the heat transfer process. Through the analysis of a series of parameters, the results provide recommendations for improving the CO2 removal efficiency and help to explore the optimal protocol for the design of bed reactors for CO2 removal.

Bed structure of CNT agglomerates in gas–solid fluidized beds

In this study, the bed structure according to the flow regime of fine particles in gas–solid fluidized beds was studied by measuring the pressure drop and heat transfer coefficient. Two types of carbon nanotube (CNT) agglomerates with behavior similar to Geldart A and Geldart B and two types of glass bead (GB) particles corresponding to Geldart A and Geldart B classification were used. CNTs fluidized in the form of agglomerates, and in some CNT agglomerates, a homogeneous expansion regime with suppressed generation of bubbles, were observed. In the homogeneous expansion regime of CNT agglomerates, the relationship between superficial velocity and voidage was consistent with the Richardson–Zaki equation, and had a higher n value than that of Geldart A particles and fumed silica agglomerates. In this regime, small-diameter channels coexisted, and due to unstable fluidization at low superficial velocity, intermittent bed collapse occurred. In the homogeneous expansion regime of GB particles, the heat transfer coefficient remained constant, and as bubbles were generated, the heat transfer coefficient increased. On the other hand, as the superficial velocity increased, the heat transfer coefficient of CNT agglomerates continuously increased, regardless of the flow regime transition. In the homogeneous expansion regime of CNT agglomerates, unlike Geldart A particles, movement of agglomerates within the bed was confirmed, and a dynamic bed structure was present, in which the movement increased according to the superficial velocity.