MP-PIC simulation of CFB riser with EMMS-based drag model

CFD-DEM Evaluation of the Clustering Behavior in a Riser-the Effect of the Drag Force Model

Riser reactors are frequently applied in catalytic processes involving rapid catalyst deactivation. Typically heterogeneous flow structures prevail because of the clustering of particles, which impacts the quality of the gas-solid contact. This phenomenon results as a competition between fluid-particle interaction (i.e., drag) and particle-particle interaction (i.e., collisions). In this study, five drag force correlations were used in a combined computational fluid dynamics-discrete element method Immersed Boundary Model to predict the clustering. The simulation results were compared with experimental data obtained from a pseudo-2D riser in the fast fluidization regime. The clusters were detected on the basis of a core-wake approach using constant thresholds. Although good predictions for the global (solids volume fraction and mass flux) variables and cluster (spatial distribution, size, and number of clusters) variables were obtained with two of the approaches in most of the simulations, all the correlations show significant deviations in the onset of a pneumatic transport regime. However, the correlations of Felice (Int. J. Multiphase Flow1994, 20, 153-159) and Tang et al. [AIChE J.2015, 61 ( (2), ), 688-698] show the closest correspondence for the time-averaged quantities and the clustering behavior in the fast fluidization regime.

Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems

Physics-based simulations are often used to model and understand complex physical systems in domains such as fluid dynamics. Such simulations, although used frequently, often suffer from inaccurate or incomplete representations either due to their high computational costs or due to lack of complete physical knowledge of the system. In such situations, it is useful to employ machine learning (ML) to fill the gap by learning a model of the complex physical process directly from simulation data. However, as data generation through simulations is costly, we need to develop models being cognizant of data paucity issues. In such scenarios, it is helpful if the rich physical knowledge of the application domain is incorporated in the architectural design of ML models. We can also use information from physics-based simulations to guide the learning process using aggregate supervision to favorably constrain the learning process. In this article, we propose PhyNet, a deep learning model using physics-guided structural priors and physics-guided aggregate supervision for modeling the drag forces acting on each particle in a computational fluid dynamics-discrete element method. We conduct extensive experiments in the context of drag force prediction and showcase the usefulness of including physics knowledge in our deep learning formulation. PhyNet has been compared with several state-of-the-art models and achieves a significant performance improvement of 7.09% on average. The source code has been made available^*.

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 %.

A Dynamic Coarse Grain Discrete Element Method for Gas-Solid Fluidized Beds by Considering Particle-Group Crushing and Polymerization

The discrete element method (DEM) coupled with computational fluid dynamics (CFD) is used extensively for the numerical simulation of gas-solid fluidized beds. In order to improve the efficiency of this approach, a coarse grain model of the DEM was proposed in the literature. In this model, a group of original particles are treated as a large-sized particle based on the initial particle distribution, and during the whole simulation process the number and components of these particle-groups remain unchanged. However, collisions between particles can lead to frequent crushing and polymerization of particle-groups. This fact has typically been ignored, so the purpose of this paper is to rationalize the coarse grain DEM-CFD model by considering the dynamic particle-group crushing and polymerization. In particular, the effective size of each particle-group is measured by a quantity called equivalent particle-group diameter, whose definition references the equivalent cluster diameter used by the energy-minimization multi-scale (EMMS) model. Then a particle-group crushing criterion is presented based on the mismatch between the equivalent diameter and actual diameter of a particle-group. As to the polymerization of two colliding particle-groups, their velocity difference after collision is chosen as a criterion. Moreover, considering the flow heterogeneity induced by the particle cluster formation, the EMMS drag force model is adopted in this work. Simulations are carried out by using a finite volume method (FVM) with non-staggered grids. For decoupling the Navier-Stokes equations, the semi-implicit method for pressure linked equations revised (SIMPLER) algorithm is used. The simulation results show that the proposed dynamic coarse grain DEM-CFD method has better performance than the original one.

3D CPFD Simulation of Circulating Fluidized Bed Downer and Riser: Comparisons of Flow Structure and Solids Back-Mixing Behavior

The difference of gas-solids flow between a circulating fluidized bed (CFB) downer and riser was compared by computational particle fluid dynamics (CPFD) approach. The comparison was conducted under the same operating conditions. Simulation results demonstrated that the downer showed much more uniform solids holdup and solids velocity distribution compared with the riser. The radial non-uniformity index of the solids holdup in the riser was over 10 times than that in the downer. In addition, small clusters tended to be present in the whole downer, large clusters tended to be present near the wall in riser. It was found that the different cluster behavior is important in determining the different flow behaviors of solids in the downer and riser. While the particle residence time increased evenly along the downward direction in the downer, particles with both shorter and longer residence time were predicted in the whole riser. The nearly vertical cumulative residence time distribution (RTD) curve in the downer further demonstrated that the solids back-mixing in the downer is limited while that in the riser is severe. Solids turbulence in the downer was much weaker compared with the riser, while the large clusters formation near the wall in the riser would hinder solids transportation ability.

Progress in coal chemical technologies of China

China’s unique energy reserve structure abundant in coal and scarce in crude oil and natural gas has promoted heavy investment on the research and development of clean coal chemical technologies during last two decades, which has turned China into a heartland for demonstrating, developing, and commercializing virtually every aspect of new coal chemical process technologies. Consequently, breakthroughs in coal gasification, indirect and direct coal-to-liquid (CTL) processes, and methanol-to-olefins (MTO) technologies are catching attention worldwide. Gasification technology for syngas production is the key to high plant availability and economic success for most coal chemical projects. During the past 20 years, both international and Chinese gasifier vendors have reaped great successes in licensing their technologies in the domestic market. Notably, the local vendors have been investing heavily on inventing and improving their technologies to suit the specific requirement of gasifying a variety of coals. The opposed multinozzle gasification technology from East China University of Science and Technology was taken as an example to demonstrate the recent development in this field. The coal chemical industry in China has witnessed several notable achievements in chemical engineering progress, namely CTL (indirect and direct) and MTO. Comprehensive reviews on topics such as catalysis, kinetics, and reactor design and process integration will be provided by leading scientists in related fields with firsthand information to showcase the contributions of Chinese researchers to chemical engineering science and technology.

Comparison of Riser-Simplified, Riser-Only, and Full-Loop Simulations for a Circulating Fluidized Bed

With the development of computing power, the simulation of circulating fluidized bed (CFB) has developed from riser-simplified simulation to riser-only simulation, then to full-loop simulation. This paper compared these three methods based on pilot-scale CFB experiment data to find the scope of application of each method. All these simulations, using the Eulerian–Eulerian two-fluid model with the kinetic theory of granular theory, were conducted to simulate a pilot-scale CFB. The hydrodynamics, such as pressure balance, solids holdup distribution, solids velocity distribution, and instantaneous mass flow rates in the riser or CFB system, were investigated in different simulations. By comparing the results from different methods, it was found that riser-simplified simulation is not sufficient to obtain accurate hydrodynamics, especially in higher solids circulating rates. The riser-only simulation is able to make a reasonable prediction of time-averaged behaviors of gas–solids in most parts of riser but the entrance region. Further, the full-loop simulation can not only predict precise results, but also obtain comprehensive details and instantaneous information in the CFB system.

Energy-minimization multiscale based mesoscale modeling and applications in gas-fluidized catalytic reactors

Gas-solid fluidization is intrinsically dynamic and manifests mesoscale structures spanning a wide range of length and timescales. When involved with reactions, more complex phenomena emerge and thus pose bigger challenges for modeling. As the mesoscale is critical to understand multiphase reactive flows, which the conventional two-fluid model without mesoscale modeling may be inadequate to resolve even using extremely fine grids, this review attempts to demonstrate that the energy-minimization multiscale (EMMS) model could be a starting point to develop such mesoscale modeling. Then, the EMMS-based mesoscale modeling with emphasis on formulation of drag coefficients for different fluidization regimes, modification of mass transfer coefficient, and other extensions are discussed in an attempt to resolve the emerging challenges. Its applications with examples of development of novel fluid catalytic cracking and methanol-to-olefins processes prove that the mesoscale modeling plays a remarkable role in improving the predictions in hydrodynamic behaviors and overall reaction rate. However, the product content primarily depends on the chemical kinetic model itself, suggesting the necessity of an effective coupling between chemical kinetics and flow characteristics. The mesoscale modeling can be believed to accelerate the traditional experimental-based scale-up process with much lower cost in the future.

Toward Constitutive Models for Momentum, Species, and Energy Transport in Gas-Particle Flows

As multiscale structures are inherent in multiphase flows, constitutive models employed in conjunction with transport equations for momentum, species, and energy are scale dependent. We suggest that this scale dependency can be better quantified through deep learning techniques and formulation of transport equations for additional quantities such as drift velocity and analogies for species, energy, and momentum transfer. How one should incorporate interparticle forces, which arise through van der Waals interaction, dynamic liquid bridges between wet particles, and tribocharging, in multiscale models warrants further study. Development of multiscale models that account for all the known interactions would improve confidence in the use of simulations to explore design options, decrease the number of pilot-scale experiments, and accelerate commercialization of new technologies.