Drag coefficient prediction for non-spherical particles in dense gas–solid two-phase flow using artificial neural network

Experimental and Numerical Studies of Fine Quartz Single-Particle Sedimentation Based on Particle Morphology

The sedimentation characteristics of quartz particles affect their separation and settling dehydration processes. Particle morphology determines the sedimentation equilibrium velocity. In this paper, the sedimentation of a single quartz particle is characterized by employing experimental and CFD-DEM approaches. SEM served to examine quartz particles measuring 30–500 μm, and they exhibited flaky–blocky morphologies with an average long–middle axis ratio of 1.6. Consistent with the SEM-detected morphological features of the quartz particles, suggested here is a simpler drag coefficient model, followed by verification of the model with experimental data. The results show that the velocity of a quartz particle in the non-settling direction had a fluctuation of ±0.2 mm/s. The fluctuation reached 0.4 mm/s at varying settlement release angles. The order in which the particles reached sedimentation equilibrium velocity during the settlement process was double-cone, single-cone, and square when the initial velocity was greater than sedimentation equilibrium velocity. Furthermore, the long–middle axis ratio of quartz particles diminished as their equilibrium sedimentation velocities rose. Given that the quartz particles ranged from 30 to 50 μm in size, the long–middle axis ratio wielded no discernible effect on the sedimentation equilibrium velocity.

Hydrodynamic Predictions of the Ultralight Particle Dispersions in a Bubbling Fluidized Bed

Particle and gas flow characteristics are numerically simulated by means of a proposed gas–particle second-order moment two-fluid model with particle kinetic–friction stress model in a bubbling fluidized bed. Anisotropic behaviors of gas–solid two-phase stresses and their interactions are fully considered by the two-phase Reynolds stress model and their closure correlations. The dispersion behaviors of the non-spherical expand graphite and spherical heavy particles are predicted by using the parameters of distributions of particle velocity, porosity, granular temperature, and dominant frequency. Compared to particles density 2700 kg/m3, ultralight particles exhibit the higher voidages with big bubbles and larger axial-averaged velocity of particles and stronger dispersion behaviors. Maximum granular temperature is approximately 3.0 times greater than that one, and dominant frequency for axial porosity fluctuations is 1.5 Hz that is 1/3 time as larger as that heavy particle.

Multi-Scale Modeling of Plastic Waste Gasification: Opportunities and Challenges

Among the different thermo-chemical recycling routes for plastic waste valorization, gasification is one of the most promising, converting plastic waste into syngas (H₂+CO) and energy in the presence of an oxygen-rich gas. Plastic waste gasification is associated with many different complexities due to the multi-scale nature of the process, the feedstock complexity (mixed polyolefins with different contaminations), intricate reaction mechanisms, plastic properties (melting behavior and molecular weight distribution), and complex transport phenomena in a multi-phase flow system. Hence, creating a reliable model calls for an extensive understanding of the phenomena at all scales, and more advanced modeling approaches than those applied today are required. Indeed, modeling of plastic waste gasification (PWG) is still in its infancy today. Our review paper shows that the thermophysical properties are rarely properly defined. Challenges in this regard together with possible methodologies to decently define these properties have been elaborated. The complexities regarding the kinetic modeling of gasification are numerous, compared to, e.g., plastic waste pyrolysis, or coal and biomass gasification, which are elaborated in this work along with the possible solutions to overcome them. Moreover, transport limitations and phase transformations, which affect the apparent kinetics of the process, are not usually considered, while it is demonstrated in this review that they are crucial in the robust prediction of the outcome. Hence, possible approaches in implementing available models to consider these limitations are suggested. Finally, the reactor-scale phenomena of PWG, which are more intricate than the similar processes-due to the presence of molten plastic-are usually simplified to the gas-solid systems, which can result in unreliable modeling frameworks. In this regard, an opportunity lies in the increased computational power that helps improve the model's precision and allows us to include those complexities within the multi-scale PWG modeling. Using the more accurate modeling methodologies in combination with multi-scale modeling approaches will, in a decade, allow us to perform a rigorous optimization of the PWG process, improve existing and develop new gasifiers, and avoid fouling issues caused by tar.

Expert-enhanced machine learning for cardiac arrhythmia classification

We propose a new method for the classification task of distinguishing atrial fibrillation (AFib) from regular atrial tachycardias including atrial flutter (AFlu) based on a surface electrocardiogram (ECG). Recently, many approaches for an automatic classification of cardiac arrhythmia were proposed and to our knowledge none of them can distinguish between these two. We discuss reasons why deep learning may not yield satisfactory results for this task. We generate new and clinically interpretable features using mathematical optimization for subsequent use within a machine learning (ML) model. These features are generated from the same input data by solving an additional regression problem with complicated combinatorial substructures. The resultant can be seen as a novel machine learning model that incorporates expert knowledge on the pathophysiology of atrial flutter. Our approach achieves an unprecedented accuracy of 82.84% and an area under the receiver operating characteristic (ROC) curve of 0.9, which classifies as "excellent" according to the classification indicator of diagnostic tests. One additional advantage of our approach is the inherent interpretability of the classification results. Our features give insight into a possibly occurring multilevel atrioventricular blocking mechanism, which may improve treatment decisions beyond the classification itself. Our research ideally complements existing textbook cardiac arrhythmia classification methods, which cannot provide a classification for the important case of AFib↔AFlu. The main contribution is the successful use of a novel mathematical model for multilevel atrioventricular block and optimization-driven inverse simulation to enhance machine learning for classification of the arguably most difficult cases in cardiac arrhythmia. A tailored Branch-and-Bound algorithm was implemented for the domain knowledge part, while standard algorithms such as Adam could be used for training.

Computational analysis of the particle size effect on the pressure profiles and type of flow regimes of TiO2 microparticles in a fluidized bed

The effect of particle size on the pressure profiles and flow regimes of the bed containing TiO2 microparticles (MPs) was investigated in a fluidized bed. The fluidization behavior of particles with mean diameters, d p , of 170, 200, 225, and 300 μm at different gas velocities, U g , was investigated both experimental and computational viewpoints. A computational fluid dynamic (CFD) model was developed by the Eulerian–Eulerian approach to evaluate the sensitivity of the Syamlal–O’Brien, and Gidaspow drag models on the predicted results of the bed pressure profiles. The results showed that with increasing particle size, the amplitude of pressure fluctuations increases and the type of flow regime in the bed tended from bubbling to slugging flow regime. The error analysis showed that the use of the Gidaspow model led to more accurate results than the Syamlal–O’Brien model in predicting the bed pressure drop and pressure fluctuations in the slugging flow regime. However, the Syamlal–O’Brien model was more suitable for predicting the pressure profiles in the bubbling flow regime. The results were more suitable for the bed containing particles of 300 μm than the beds with d p  ≤ 225 μm. The highest and lowest deviations between the experimental data and simulation outputs were obtained at U g of 0.295 and 0.650 m/s, respectively. The findings confirmed that the mutual effects existed between the d p pressure profiles, and the type of flow regimes in the bed.

Computational Modeling of Fluidized Beds with a Focus on Pharmaceutical Applications: A Review

The fluidized bed is an essential and standard equipment in the field of process development. It has a wide application in various areas and has been extensively studied. This review paper aims to discuss computational modeling of a fluidized bed with a focus on pharmaceutical applications. Eulerian, Lagrangian, and combined Eulerian-Lagrangian models have been studied for fluid bed applications with the rise of modeling capabilities. Such models assist in optimizing the process parameters and expedite the process development cycle. This paper discusses the background of modeling and then summarizes research papers relevant to pharmaceutical unit operations.

Graph convolutional networks applied to unstructured flow field data

Many scientific and engineering processes produce spatially unstructured data. However, most data-driven models require a feature matrix that enforces both a set number and order of features for each sample. They thus cannot be easily constructed for an unstructured dataset. Therefore, a graph based data-driven model to perform inference on fields defined on an unstructured mesh, using a graph convolutional neural network (GCNN) is presented. The ability of the method to predict global properties from spatially irregular measurements with high accuracy is demonstrated by predicting the drag force associated with laminar flow around airfoils from scattered velocity measurements. The network can infer from field samples at different resolutions, and is invariant to the order in which the measurements within each sample are presented. The GCNN method, using inductive convolutional layers and adaptive pooling, is able to predict this quantity with a validation R 2 above 0.98, and a Normalized Mean Squared Error below 0.01, without relying on spatial structure.

Hydrodynamics of Irregular-Shaped Graphite Particles in Coaxial Two-Phase Jet Flow

Expanded graphite particle is characterized by the low density in comparison with those of bead glass and copper particles. Hydrodynamics of the irregular-shaped graphite particle swirling flows in a coaxial chamber are investigated via an improved kinetic frictional stress model. A drag force coefficient considering the effects of irregular shapes based on the artificial neural network algorithm is adopted to describe the momentum transfer between nonspherical particles and gas phases. The proposed model, algorithm, and source code for modeling and simulation are validated by measurement using spherical glass beads, and acceptable agreement is obtained. Lower sphericity particles enhance the anisotropic particle dispersions and induces the redistributions of the Reynolds stresses of the two-phase flow. Irregular-shaped particles are more sensitive to the gas followability instead of own inertia, whereas spherical particles are easier to be affected by the inlet effects. The interlock force between nonspherical particles takes great effect on particle flow than the spherical particle. The axial-axial normal stresses of sphericities of 0.63 and 0.72 are approximately 3.4 times larger than those of shear stress of spherical particle, and their axial velocities locating at near central regions are 3.0 times larger than those of sphericities.

Possibilities and Limits of Computational Fluid Dynamics-Discrete Element Method Simulations in Process Engineering: A Review of Recent Advancements and Future Trends

Fluid-solid systems play a major role in a wide variety of industries, from pharmaceutical and consumer goods to chemical plants and energy generation. Along with this variety of fields comes a diversity in apparatuses and applications, most prominently fluidized and spouted beds, granulators and mixers, pneumatic conveying, drying, agglomeration, coating, and combustion. The most promising approach for modeling the flow in these systems is the CFD-DEM method, coupling computational fluid dynamics (CFD) for the fluid phase and the discrete element method (DEM) for the particles. This article reviews the progress in modeling particle-fluid flows with the CFD-DEM method. A brief overview of the basic method as well as methodical extensions of it are given. Recent applications of this simulation approach to separation and classification units, fluidized beds for both particle formation and energy conversion, comminution units, filtration, and bioreactors are reviewed. Future trends are identified and discussed regarding their viability.