Gas-solid Flow System Research Articles

Simultaneous measurement of solid concentration and solid moisture in gas–solid two-phase flow using a dual-modality electrical sensor

Abstract In a gas–solid two-phase flow system, simultaneous measurement of solid concentration and solid moisture is essential for some applications. However, during the measurement process, solid concentration and solid moisture are coupled in the measurement signals when the measurement signal is sensitive to both solid concentration and moisture. This makes it challenging to measure them simultaneously in the same measurement field. To solve this issue, a novel measurement method is proposed in this study, which uses a dual-modality electrical sensor to achieve simultaneous measurement of solid concentration and solid moisture in gas–solid two-phase flow. The dual-modality electrical sensor features a simple structure and captures measurement signals of capacitance and angle of dielectric loss within the same measurement section. With the above signals which are both sensitive to solid concentration and moisture, an iterative correction calculation method is adopted to achieve the simultaneous measurement of solid concentration and solid moisture. During the calculation process, the measured parameters are decoupled and continuously corrected until the related error values meet the threshold requirement. Numerical simulation experiments under three typical distributions and physical experiments under roping flow are carried out to verify the proposed measurement method. The results prove the effectiveness of the proposed measurement method in the simultaneous measurement of solid concentration and solid moisture for gas–solid two-phase flow.

Physics-informed data-driven unsteady Reynolds-averaged Navier–Stokes turbulence modeling for particle-laden jet flows

With the assistance of deep learning (DL), we present a framework for predicting the turbulent eddy viscosity in unsteady Reynolds-averaged Navier–Stokes (URANS) simulations for particle-laden jet flows. We report a complete workflow from identifying the input flow and particle quantities in the training phase to predicting the flow and particle fields in the testing phase. The framework incorporates a deep neural network model, also known as multi-layer perceptrons, into the momentum equations of the Euler–Lagrangian gas–solid flow system. A data-driven, physics-informed DL approach was employed to predict the modeled turbulent eddy viscosity field, formulated as a function of the instantaneous flow and particle quantities. In the training phase, these regression functions were trained with an existing high-fidelity direct numerical simulation database. In the testing phase, the trained model was then used to predict the instantaneous local eddy viscosity to update the closure term and to solve the URANS equations iteratively. A series of round, turbulent particle-laden jets in a co-flow with various Stokes numbers were assessed, including those beyond the range of conditions employed for training. The proposed DL–URANS model was found to provide enhanced accuracy for predicting both flow and particle quantities when compared with the baseline URANS simulation.

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.

On reduced-order modeling of gas–solid flows using deep learning

Reduced-order models (ROMs) have been extensively employed to understand complex systems efficiently and adequately. In this study, a novel parametric ROM framework is developed to produce Eulerian–Lagrangian simulations. This study employs two typical parametric strategies to reproduce the physical phenomena of a gas–solid flow by predicting the adequate dynamics of modal coefficients in the ROM: (i) based on the radial-basis function (RBF) interpolation, termed ROM-RBF and (ii) based on a long–short term memory (LSTM) neural network, termed ROM-LSTM. In the ROM, an advanced technique, namely, Lanczos-based proper orthogonal decomposition (LPOD), is employed to efficiently transform numerical snapshots into the modal coefficients. Validation tests are conducted in a typical gas–solid flow system such as a spouted bed. The coherent structures of the gas–solid flows are shown to be captured by the LPOD technique. Besides, in comparison with the high-fidelity simulations, our proposed ROMs are shown to simulate the gas–solid flows by significantly reducing the calculation time by several orders of magnitude and faithfully predicting the macroscopic properties. In particular, compared to the ROM-RBF, the ROM-LSTM can capture the flow fields more accurately within the gas–solid flows.

Tuning the selectivity of the CO2 hydrogenation reaction using boron-doped cobalt-based catalysts.

Direct CO2 hydrogenation to value-added chemicals is a promising path toward realizing the "carbon-neutral" goal. However, controlling the selectivity of CO2 hydrogenation toward desired products (e.g., CO and CH4) using non-precious metal-based catalysts is important but challenging. It is imperative to explore catalysts with high activity and stability. Herein, boron-doped cobalt nanoparticles supported on H-ZSM-5 were devised for CO2 hydrogenation to produce CO in a gas-solid flow system. Our results demonstrate that boron doping not only increases the CO2 adsorption capability of the catalyst but also optimizes the electronic state of Co for CO desorption during hydrogenation process. As a result, the boron-doped cobalt catalysts displayed an enhanced CO selectivity of 94.5% and a CO2 conversion rate of 45.6%, which is much higher than that of Co-ZSM-5 without boron doping. This study shows that the strategic design of metal borides is important for controlling the selectivity of desired products in the CO2 hydrogenation reaction.

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.

Application of nonlinear dynamics analysis to gas-solid flow system in horizontal pneumatic conveying of plastic pellets

Chaotic invariant and recurrence quantification analysis measures have characterised fully developed gas-solid flow in horizontal pneumatic conveying of plastic pellets. These measures describe the complexity in phase spaces (attractors) and recurrence plots, reconstructed from pressure and bottom arc-shaped electrostatic signals to characterise the behaviour of flow patterns. Different flow patterns were identified using high-speed video imaging of a transparent pipeline and classified at several operating conditions in a flow pattern map and state diagram. Recurrence plots were analysed for the identified flow patterns, which showed different qualitative structures. The chaotic invariant and recurrence quantification analysis measures were correlated with the state diagram, indicating that the fluctuations of pressure senor and electrostatic sensor signals can classify the flow patterns at different operating conditions. Combining the analysis measures for electrostatic signals can indicate whether the flow condition is above, near or below the minimum energy consumption operating conditions.

Fragmentation and abrasion of solid particles in cyclones of CFB reactors: Phenomenology and kinetic model constitution

Particulate material in gas-solid flow systems is under constant mechanical stress due to the collisions of solid particles with the internal walls of the equipment and inter-particle collisions. These collisions cause attrition, resulting in a reduction in particle diameter. This study aims to analyze this phenomenon by evaluating the particle size distribution flowing in a loop in a cyclone. Samples were collected after each pass in the cyclone to obtain the temporal variation of particle size distribution. Increases in the inlet velocity increased the generation of fine particles, consequently reducing the collection efficiency in the cyclone. In addition, a kinetic model was built from the experimental database, and it enabled the prediction of a size reduction of FCC catalyst particles in a CFB reactor. • Effects of fragmentation and abrasion on the cyclone performance. • Phenomenology of fragmentation and abrasion in cyclones operating at high inlet velocities. • Reduction of collection efficiency in cyclones due to particles fragmentation. • Kinetic model constitution for fragmentation and abrasion in cyclones. • Prediction of fragmentation in cyclones operating in a circulating fluidized beds.

Investigation on gas–solid flow regimes in a novel multistage fluidized bed

Abstract Gas–solid flow regime in a novel multistage circulating fluidized bed is investigated in this study. Pressure fluctuations are first sampled from gas–solid flow systems and then are analyzed through frequency and time-frequency domain methods including power spectrum and Hilbert–Huang transform. According to the flow characteristics obtained from pressure fluctuations, it is found that the gas–solid motions in the multistage circulating fluidized bed exhibit two dominant motion peaks in low and high frequencies. Moreover, gas-cluster motions become intensive for the multistage circulating fluidized bed in comparison with the fast bed. Unlike the traditional methods, the fuzzy C-means clustering method is introduced to objectively identify flow regime in the multistage circulating fluidized bed on the basis of the flow characteristics extracted from bubbling, turbulent, fast, and multistage fluidized beds. The identification accuracy of fuzzy C-means clustering method is first verified. The identification results show that the flow regime in the multistage circulating fluidized bed is in the scope of fast flow regime under examined conditions. Moreover, the results indicate that the consistency of flow regime between two enlarged sections exists. In addition, the transition onset of fast flow regime in the multistage circulating fluidized bed is higher than that in the fast bed.

Compressibility of granular fluids

The effect of granular pressure on the fluctuation of the granular volume fraction is investigated experimentally in a gas–solid flow system. Granular compressibility, defined by the response of granular volume fractions in granular pressure, is also measured. It is found that there exists a critical granular pressure beyond which the transition from the coexistence of a liquid-like granular phase and a gas-like granular phase to homogenous granular fluids takes place. The transition from a heterogenous to homogenous granular phase is characterized by an abrupt change in the granular compressibility. To describe the characteristic change in compressibility, a granular hydrostatic model is formulated by considering both collisional and non-collisional interactions. The model predictions agree well with experimental results.

Study on particle cluster behavior in the multistage circulating fluidized bed

Particle clusters play a significant role in gas-solid flow systems. Cluster motions are experimentally studied in a multistage circulating fluidized bed. Cluster characteristics including spatial distribution, breakup process, and size are discussed. Dynamic parameters (frequency, duration, and time fraction) are also obtained to characterize cluster behavior by employing the digital image analysis method. The results show that cluster motions gradually weaken along radial and axial directions in the enlarged section. Cluster breakup tends to increase in the enlarged section due to gas turbulence and cluster collision. Cluster frequency, duration, and time fraction decrease with the increasing gas velocity but increase with the increasing solid mass flux. Compared with the conventional riser, cluster size reduces in the enlarged section. Furthermore, the analysis results from cluster dynamic parameters indicate that cluster motions are restricted under the low solid loading.

An approach to simulate gas-solid flow systems with process controllers

Gas-solid flow systems usually suffer from complex internal states and unstable operations, thus internal state monitoring and process control are both essential for efficient and stable operations. In this work, a versatile mathematical approach is developed for the collaborative simulation of both describing the transient internal flow details at the microscale and simulating the response of dynamic process controllers at the macroscale, by means of combining a CFD model and a process controller simulation based on the framework of ANSYS-Fluent and MATLAB. The model is applied to a symmetrical Circulating fluidised bed (CFB) for effectiveness demonstration, where the solid maldistribution can be mitigated through online regulation of L-valve aeration rate using the process controller. Then CFBs with process controllers compared with the case without any process controllers, indicating the versatile capability of this approach in effective real-time control to complex systems. This work provides a new versatile method to study and optimise complex CFB processes equipped with controllers in practice and a key foundation to digital twin in future CFB operation.

Development of ring-shaped electrostatic coupled capacitance sensor for the parameter measurement of gas-solid flow

This paper develops a novel non-intrusive ring-shaped electrostatic coupled capacitance sensor (ECCS) for the parameter measurement of gas-solid flow to eliminate the temperature drift of traditional capacitance sensor and to improve the reliability of velocity measurement. In ECCS, one source electrode and two detection electrodes are housed in a sensing head to simultaneously derive two pairs of capacitance and electrostatic signals, which can achieve the simultaneous measurement of the particle velocity, concentration and mass flow rate within the same sensing space of gas-solid flow system. The effects of the isolation electrodes on the capacitance sensitivity and the temperature drift of the sensor standing capacitance are further investigated. Then, a weighted velocity is determined by fusing the capacitance correlation velocity and the electrostatic correlation velocity based on the correlation coefficients, which are useful for the reliable measurement of gas-solid flow. Finally, experiments are carried out to test the performance of the developed ring-shaped ECCS. Results demonstrate that the developed ECCS triples the capacitance sensitivity for the radial position from -15 mm ∼15 mm. The temperature drift of the capacitance signal is less than 0.075 mV/oC from the room temperature to 65 oC, and thus the sensor standing capacitance is almost impervious to the temperature. After calibrating the relationship between the particle concentration and the capacitance signal, the developed ECCS can measure the particle mass flow rate with a relative error less than ±8%.

Gas–solid flow in a diffuser: Effect of inter-particle and particle–wall collisions

This article investigates the role of the specularity coefficient (φ, the extent of the energy dissipation due to particle–wall collisions), inter-particle restitution coefficient (epp, the extent of the energy dissipation due to inter-particle collisions), and four combinations of these variables on the hydrodynamics, and the pressure recovery of the dilute gas–solid suspension in a diffuser. The investigation applies the two-fluid modeling approach along with the kinetic theory of the granular flow. The present investigation’s findings indicate that an increase in φ or a reduction in epp reduces the pressure recovery by weakening the reverse momentum transfer phenomenon, which is recognized as the primary means for the pressure recovery. Besides, in a gas–solid flow system, a higher φ or smaller epp enhances the particles’ trapping in the recirculation zone. The recirculation zone’s strength and size increase as φ increases or epp reduces. Moreover, an increase in the wall–particle and inter-particle interactions strengthens the sidewise displacement of the particles. The effect of the wall–particle and inter-particle interactions are insignificant for extremely small solid loading.

Coarse-grid simulations of full-loop gas-solid flows using a hybrid drag model: Investigations on turbulence models

The effect of fluid turbulence models on the coarse-grid simulation for the mesoscale gas-solid flow system is not as clear as that for gas-liquid/single flows. In this study, the effect of different turbulence models on predictions of riser-only, turbulent and bubbling flow hydrodynamics is quantified. Then the selected turbulence models are examined by coarse-grid simulations in a full-loop circulating fluidized bed (CFB) with a proposed hybrid drag model which integrates two SGMs and Hulin-Gidaspow model. Results show that the standard k-epsilon and k-omega models fail to capture a correct fluidization pattern in riser. Meanwhile, the RNG and realizable k-epsilon models and SST k-omega model enable satisfactory predictions which are highly comparable to discrete model predictions and experiments over different flow regimes. However, the realizable k-epsilon model produces more pronounced flow fluctuations causing computational instability. Using superior turbulence closures, full-loop simulations with the proposed hybrid model can predict desirable hydrodynamics.

Visualization study on the coarse graining DEM for large-scale gas–solid flow systems

Gas–solid flows are ubiquitous in industrial systems. The coupled model of the discrete element method (DEM) and computational fluid dynamics (CFD) is one of the techniques for the simulation of the gas–solid flows. To enhance the applicability of the DEM-CFD method, the coarse graining DEM has been developed to simulate large-scale powder systems. The coarse graining DEM is a scaling law model and hence can simulate large-scale systems using a smaller number of particles than the actual one. Although the coarse graining DEM enables to effectively simulate large-scale powder systems on a single PC, visualized image is lack of reality due to reduced spatial resolution. Here a novel visualization method is developed to produce realistic images. In the proposed technique, the virtual particles are located by considering the motion and location of the coarse grain particles. To show the adequacy of the proposed method, two types of visualization based on the computational results are made: a blow-up of powder due to injected gas flow and a spouted bed. In these systems, the visualized images are shown to be improved by the proposed visualization technique. The effectiveness of the proposed method is proved by the agreement of the images based on the calculation results between the standard DEM-CFD method and coarse graining DEM.

On artificial density treatment for the pressure Poisson equation in the DEM-CFD simulations

The discrete element method (DEM) coupled with computational fluid dynamics (CFD) is regarded to be a standard approach in a numerical calculation of gas-solid flow. When the DEM-CFD method is applied to the simulation of industrial systems, we cannot shy away from the modeling of arbitrary shape wall boundaries. Very recently, we have developed an innovative wall boundary model in the DEM-CFD simulations, where the signed distance function (SDF) and the immersed boundary method (IBM) are used for the wall boundary in the DEM and CFD. The SDF makes it possible to create an arbitrary shape wall boundary based on the scalar field. The IBM can be used for the wall boundary model by reflecting the number of the SDF points on the local volume fraction in CFD grid. Thus, the arbitrary shape wall boundary can be easily created in the DEM-CFD method by combining the SDF with the IBM. On the other hand, the authors' group has pointed out the necessity of the density scale technique in CFD. In the density scale technique, the density scale coefficient is considered in the calculation of the pressure Poisson equation. When the SDF/IBM is applied to CFD without density scaling technique, an unnatural fluid flow might occur through the wall boundary object. The effect of the density scaling coefficient on a gas-solid flow has not been examined so far. From this background, the influence of the density scale coefficient on a gas-solid flow is investigated in the present study. Through this study, introduction of the density scaling technique is designated to be crucial when the IBM is used for the wall boundary in the DEM-CFD simulation. The density scaling coefficient is shown to be decided not by theory but by experience, and the value should become 100 to 1000 in a typical gas-solid flow system. Besides, it is illustrated that the calculation cost is not influenced by the value of the density scale coefficient in the DEM-CFD simulations.

Study of wall-to-bed heat transfer in circulating fluidized bed riser based on CFD simulation

Circulating fluidized bed (CFB) riser reactors are widely used in several industrial gas–solid flow systems to achieve a high reaction rate and a high heat of reaction. Thus, understanding wall-to-bed heat transfer is essential for optimizing CFB operation and design. This study aims to analyze the wall-to-bed heat transfer in a CFB riser reactor. A two-fluid model with the kinetic theory of granular flow was used to solve the hydrodynamics and heat transfer behavior in the bed. The wall-to-bed heat transfer coefficient is strongly dependent on the CFB operating conditions, but correlations for the wall-to-bed heat transfer coefficient are often defined in terms the suspension density, which is not an operating parameter. This makes the correlations difficult to use because the solid fraction in the system must be known. Thus, in this study, a correlation was developed that defines the heat transfer coefficient in terms of two operating parameters: the solid circulation rate (Gs) and the superficial gas velocity (Ug). The developed correlation exhibited fairly accurate prediction for several particle sizes because this correlation is based on fluid catalytic cracking (FCC) particles having a specific diameter. To better address the effect of different particle sizes, the correlation was further modified to be a function of the particle diameter in addition to Ug and Gs. The modified correlation can predict the simulated and experimental data within a ±20% deviation and an averaged absolute relative error of 5.72%.

DESIGN OF AN EXPERIMENTAL DEVICE FOR THE MEASUREMENT OF THE COEFFICIENT OF RESTITUTION

In this work an experimental device was designed and built to measure the coefficient of restitution of particles of different type and size. The aim of the study is to analyze the dependence of the coefficient of restitution with the impact velocity, the shape and mass of the particles. Three types of particles of different origin and shape were used: lentils, urea granules and polyethylene pellets. Besides, two impact materials were evaluated: steel and polycarbonate. The images were captured with a high resolution camera, recording the moment at which the particles impact and bounce on the surface. Image processing software was used to obtain the position of the particle at the instants before and after the impact. The particle velocities before and after the collision were calculated and the coefficient of restitution of the particles were obtained. The experimental results showed the dependence of the coefficient of restitution with the studied variables, providing useful information about the energy dissipation for numerical simulations and a breakthrough in the study of gas-solid flow systems.

An EMMS drag model for coarse grid simulation of polydisperse gas–solid flow in circulating fluidized bed risers

Polydisperse gas–solid systems are more common in both industry and the natural world than their monodisperse counterparts. This paper aims to investigate the hydrodynamics of polydisperse gas–solid flow systems by extending the energy minimization multiscale (EMMS) drag model. The continuous particle size is discretized into several characteristic sizes, by which the polydisperse particles are classified into several discrete groups. A polydisperse EMMS drag model is then developed by treating these particle groups as solid phases. Finally, the proposed drag model is coupled with the multifluid model to simulate the hydrodynamics of polydisperse gas–solid flow in CFB risers. Extensive simulations are conducted to validate the effectiveness of the polydisperse EMMS drag model and investigate the effects of gas–particle drag models, kinetic theories, and particle–particle drag models on the simulation results. It was found that (i) the polydisperse EMMS drag model can predict the concentration and particle size distribution of polydisperse gas–solid flows better than the traditional drag model; (ii) the particle–particle drag force has an important influence on the mixing and segregation of different particles whereas the particulate phase stress has only a very minor effect, especially when the superficial gas velocity is low. The second finding highlights the need for a suitable particle–particle drag model that considers the effects of mesoscale structures, which will be the topic of our future study.