Industrial Processes Research Articles

Fabrication, modeling, and testing of a prototype thermal energy storage containment

Abstract Increasing penetration of variable renewable energy resources requires the deployment of energy storage at a range of durations. Long-duration energy storage (LDES) technologies will fulfill the need to firm variable renewable energy resource output year-round; lithium-ion batteries are uneconomical at these durations. Thermal energy storage (TES) is one promising technology for LDES applications because of its siting flexibility and ease of scaling. Particle-based TES systems use low-cost solid particles that have higher temperature limits than the molten salts used in traditional concentrated solar power systems. A key component in particle-based TES systems is the containment silo for the high-temperature (> 1100 °C) particles. This study combined experimental testing and computational modeling methods to design and characterize the performance of a particle containment silo for LDES applications. A laboratory-scale silo prototype was built and validated a congruent transient finite element analysis model. The performance of a commercial-scale silo was then characterized using the validated model. The commercial-scale model predicted a storage efficiency in excess of 95% after five days of storage with a design storage temperature of 1200 °C. Insulation material and concrete temperature limits were considered as well. The validation of the methodology means the FEA model can simulate a range of scenarios for future applications. This work supports the development of a promising LDES technology with implications for grid-scale electrical energy storage, but also for thermal energy storage for industrial process heating applications.

Effect of Impregnation of PEDOT:PSS in Etched Aluminium Electrodes on the Performance of Solid State Electrolytic Capacitors

Electrolytic capacitors store larger amounts of energy thanks to their thin dielectric layers and enlarged surface area. However, the benefits of using a liquid electrolyte are at the expense of the possibility of leakage, evaporation, or rupture of the device over time. As a solution, solid electrolytes, such as conductive polymers, substitute the liquid ones decreasing the internal resistance and enlarging the lifetime of these devices. PEDOT:PSS is a widely used conductive polymer in the formation of solid electrolytic capacitors. However, using the enlarged surface of the porous electrodes efficiently requires industrial processes, the efficacy of which has not been explored. In this work, porous aluminium electrodes with dielectric layers of different thicknesses were coated with PEDOT:PSS at different levels of doping in order to study the efficiency of the production of solid electrolytic capacitors in industry. The combination of odd random phase electrochemical impedance spectroscopy (ORP-EIS) with surface characterization techniques (SEM-EDX, GDOES) formed a methodology that allowed the study of both the electrical properties and the level of impregnation for these model systems. All samples consisting of a porous aluminium electrode with an amount of PEDOT:PSS deposited on top resulted in an inefficient degree of penetration between the two electrodes. However, the electrochemical analysis proved that the use of dopants produces systems with the highest capacitive properties. Consequently, the evolution towards better solid electrolytic capacitors does not rely solely on the proper coverage of the porous electrodes, but on the proper electrical properties of the PEDOT:PSS within the pores.

Exploring the essential features influencing the synthesis of methylenedianiline to support industrial processes

The most important intermediate of methylene diphenyl diisocyanate (MDI), the most widely and quantitatively produced isocyanate in the world, is methylenedianiline (MDA). MDA is industrially produced from the reaction of aniline and formaldehyde catalysed by inorganic acids, most commonly HCl. The reaction parameters used during the synthesis of MDA have a fundamental impact on the quality parameters of the resulting MDA product mixture, and thus on the properties of the final MDI product mixture as well. Although MDA is an important industrial intermediate, currently its synthesis at the industrial level is based on empirical rules, knowledge and rule of thumbs, the effects of production parameters and their magnitude on product properties are not known and not published in the literature. In this study, the correlations between the independent operating parameters and dependent product quality parameters characterizing the MDA mixture were explored by developing different regression models with the use of experimental laboratory synthesis data in order to support the industrial synthesis process with easily deployable models with satisfactorily high accuracy. After investigating the laboratory experiment data from a total of 46 individual laboratory experiments, it was found that with machine learning models almost all of the independent parameters can be described with satisfactory accuracy. The models presented in this work demonstrate that it is possible to develop models that can be used to adjust the ring distribution, isomer ratios and selectivity of the reaction with minimising by-product quantities according to market demands or to different objective functions by fine-tuning the production parameters., thus achieving an optimal product portfolio and operating cost for the industrial process as well.

Causal intervention graph neural network for fault diagnosis of complex industrial processes

With the development of industry and manufacturing, the mechanical structures of equipment have become intricate and complex. Due to the interaction between components, once a failure occurs, it will propagate through the industrial processes, resulting in multiple sensor anomalies. Identifying the root causes of faults and eliminating interference from irrelevant sensor signals are critical issues in enhancing the stability and reliability of intelligent fault diagnosis. The components of industrial processes and their interactions can be represented by a structural attribute graph. The causal subgraph formed by fault signals determines the fault mode, while irrelevant sensor signals constitute a non-causal subgraph. The structure of non-causal subgraphs is relatively simple, and graph neural networks tend to use this part as a shortcut for prediction, leading to a significant decrease in prediction accuracy. To address this issue, a causal intervention graph neural network (CIGNN) framework is proposed. First, the sensor signals are constructed into structural attribute graphs using an attention mechanism. Due to causal and confounding features are highly coupled in graphs, explicitly decoupling them is almost impossible. Then, we design an instrumental variable to implement causal intervention to mitigate the confounding effect. Experimental results on two complex industrial datasets demonstrate the reliability and effectiveness of the proposed method in fault diagnosis.

Causal intervention graph neural network for fault diagnosis of complex industrial processes

With the development of industry and manufacturing, the mechanical structures of equipment have become intricate and complex. Due to the interaction between components, once a failure occurs, it will propagate through the industrial processes, resulting in multiple sensor anomalies. Identifying the root causes of faults and eliminating interference from irrelevant sensor signals are critical issues in enhancing the stability and reliability of intelligent fault diagnosis. The components of industrial processes and their interactions can be represented by a structural attribute graph. The causal subgraph formed by fault signals determines the fault mode, while irrelevant sensor signals constitute a non-causal subgraph. The structure of non-causal subgraphs is relatively simple, and graph neural networks tend to use this part as a shortcut for prediction, leading to a significant decrease in prediction accuracy. To address this issue, a causal intervention graph neural network (CIGNN) framework is proposed. First, the sensor signals are constructed into structural attribute graphs using an attention mechanism. Due to causal and confounding features are highly coupled in graphs, explicitly decoupling them is almost impossible. Then, we design an instrumental variable to implement causal intervention to mitigate the confounding effect. Experimental results on two complex industrial datasets demonstrate the reliability and effectiveness of the proposed method in fault diagnosis.

Accelerated MPC: A real-time model predictive control acceleration method based on TSMixer and 2D block stochastic configuration network imitative controller

Modern industrial processes demand real-time model predictive control (MPC) method within the constraints of limited computing resources. This paper introduces an accelerated MPC method to address the issue. Specifically, we employ the time series mixing model (TSMixer) to construct a predictive model capable of accurately forecasting the behavior of the system under control. Furthermore, we extend the capabilities of TSMixer by integrating a feature classification model (TSMixer with FCM). This enhanced version takes into account both future information and static variables, resulting in superior accuracy compared to the transformer model while maintaining a significantly smaller parameter count. Finally, to further enhance MPC’s responsiveness, we develop a two-dimensional block stochastic configuration network (2D-BSCN) imitative controller. This innovative network clones the behavior of rolling optimization, effectively replacing online optimization to reduce computational time. The experimental results show that the accelerated MPC has an excellent performance in numerical simulation and actual industrial processes. Notably, the algorithm achieves a smaller FLOPs and tracking time than other state-of-the-art models, well within the requirement for industrial process control.

Comparative Analysis of Zero-Carbon Supply Solutions for Mid-to-Low Temperature Process Heat Using Nuclear Energy

Energy consumption and carbon emissions from industrial heating processes constitute a significant and indispensable proportion of industrial production. Therefore, advancing the decarbonization of industrial production, particularly to optimize industrial heating processes, is of considerable importance in achieving global environmental sustainability. This study evaluates long-distance steam transport (LDST) and long-distance hot water transport (LDHWT) as zero-carbon supply schemes for mid-to-low temperature process heat. The heat is sourced from the existing Hainan Nuclear Power Plant using indirect heat exchange, ensuring safety for the long-distance transport medium. The schemes were applied in the Yangpu Industrial Park and comparatively analyzed based on energy efficiency and economic viability. The results indicate that both schemes exhibit comparable energy efficiencies, with LDHWT offering a slight economic advantage. However, beyond this case study, LDHWT becomes increasingly advantageous with longer transport distances and encourages factories to lower their required temperatures through technological modifications. By evaluating these schemes, this study aimed to inform future sustainable process heat decisions and contribute to reducing carbon emissions in global industrial operations.

Characterization of protease-producing bacteria from garden soil and antagonistic activity against pathogenic bacteria

Bacteria play an essential role in various industrial processes, including food, medicine, and detergents, due to their ability to produce protease, a hydrolytic enzyme. This study aimed to identify and characterize protease-producing bacterial strains isolated from garden soil, evaluate their antibiotic susceptibility, and optimize conditions for enhanced protease production. Using skimmed milk agar medium and the gelatin hydrolysis method, 7 bacterial strains (S1, S2, S3, S4, S5, S6, and S7, respectively) were identified for protease production in garden soil. The bacteria were identified based on morphological studies, biochemical characterization, and the BIOLOG™ system, and the isolates were determined to be Bacillus cereus (S1), B. thuringiensis (S2), B. subtilis (S3), B. amyloliquefaciens (S4), Pseudomonas aeruginosa (S5), Macrococcus brunensis (S6), and B. schlegelii (S7). The antibiotic susceptibility test, performed using the Kirby-Bauer agar disc diffusion method, revealed that all isolates were resistant to penicillin, nitrofurantoin, and metronidazole. Additionally, B. subtilis and P. aeruginosa demonstrated antagonistic activity against pathogens. Optimal conditions for protease production were found to be a temperature range of 30°C to 40°C and a pH range of 6.5–7.5. Under these conditions, B. cereus (2.19 U/ml) and B. thuringiensis (2.12 U/ml) exhibited the highest protease activity. Further characterization of protease activity in B. cereus, P. aeruginosa, and M. brunensis under different physiological parameters revealed that maximum activity occurred within a pH range of 5.0–6.0 and temperatures between 30°C and 40°C. Protease activity increased in the presence of calcium (CaCl2) but decreased with the addition of urea. These findings underscore the significant industrial applications of proteases produced by these bacterial strains.

Zinc cations incorporated in Silicalite-1 zeolite assist in anchoring atomically dispersed platinum catalyst for boosted and robust propane dehydrogenation

The utilization of Pt-based catalysts in the catalytic conversion of propane to propylene (PDH) remains pivotal in industrial processes. However, the scarcity of Pt and its high cost necessitates innovative strategies to enhance Pt dispersion and catalytic performance. In this study, we present an approach involving the in-situ incorporation of Zn cations into Silicalite-1 (S-1) zeolite through one-pot hydrothermal synthesis, which facilitates the anchoring of atomically dispersed Pt atoms. The synthesized 0.5Pt/Zn@S-1 catalyst exhibits the dehydrogenation performance of 25 % propane for approximately 34.2 % propane conversion and 95.1 % propylene selectivity (propylene turnover rate of 14.2 s−1), coupled with robust stability of up to 60 h without distinct deactivation under a WHSV of 49.1 h−1. Comprehensive characterizations reveal the formation of atomically dispersed Pt-Zn catalytic sites. The Pt atoms are incorporated with the Zn cations and dense silanol nests. This unique configuration facilitates a synergistic effect between Pt and Zn for dehydrogenation.

Experimental Study of the Rising Behavior of a Single Bubble in Shearshinning Fluids

Background: Non-Newtonian gas-liquid two-phase flows are often seen in industrial processes such as petroleum, chemical, and food engineering. The efficiency of mass and heat transfer between phases is significantly impacted by bubble rise motion in liquids. Therefore, it is crucial to deeply study the hydrodynamic characteristics of a bubble rising in non-Newtonian fluids to improve the transfer efficiency between phases and to enhance the operational efficiency of bubbling equipment. Methods: To understand the rising characteristics of a bubble in non-Newtonian fluids, a single bubble rising in shear-thinning fluids was experimentally studied using a high-speed camera. The effects of xanthan gum (XG) concentration and bubble diameter on bubble shape, trajectory, and terminal velocity were investigated. Results: Bubble terminal velocity increased with an increase in the bubble diameter and a decrease in XG concentrations. The increase rate of bubble terminal velocity varied with an increase in bubble diameter for the bubbles with different diameters and XG concentrations for the solutions with varying XG concentrations. For solutions with the same XG concentration, the Galilei and Eötvös numbers for a small bubble were relatively small but relatively large for a large bubble. Thus, the rise motion of a bubble in XG solutions becomes unsteady with an increase in bubble diameter and a decrease in XG concentrations. The unsteady characteristics of bubble motion decrease with an increase in the XG concentration of solutions. Conclusion: It was found that the influence of XG concentrations on bubble motion depends on bubble diameter since the magnitude of bubble diameter has an essential effect on the shear-thinning effect of solutions. An increase in bubble terminal velocity is mainly caused by an increase in buoyancy (i.e., bubble diameter) rather than a decrease in the apparent viscosity of solutions.

Rancidity development in oat during industrial processing

To improve the shelf-life of food products rich in lipids, it is important to minimize the lipid oxidation that occurs during processing, since this oxidation not only decreases shelf-life but also induces off-flavors. To evaluate the oxidation of lipids in the production of one β-glucan-rich and one protein-rich food supplement made from oats (Avena sativa), the neutral and polar lipid profile, hexanal content, peroxide value, avenanthramides and lipase activity were measured at all production steps. The concentration of antioxidants in the form of avenanthramides was also investigated. This study shows that oat lipases were fully inactivated in the wet processing part of the processing line, and the total lipid content per dry weight increased with the progression of steps toward the oat protein product. Processing had no apparent effect on avenanthramide levels. Lipid oxidation markers increased in certain processing steps. The most significant increase in hexanal and peroxide values could be seen in the spray-drying step of the process, while a much smaller increase was seen for the drum-drying. The main conclusion is that the most important factor to prolong the shelf-life of the product is to minimize the oxidation in the drying step.

Hydration and deliquescence behavior of calcium chloride hydrates

The phase transitions of calcium chloride between various hydrates and solid–liquid phase transitions are common in many natural and industrial processes. Recent studies have revealed some discrepancies in investigating the hydration and deliquescence of calcium chloride using different methods. In this study, water vapor sorption analysis and Raman measurements on CaCl2·2H2O and CaCl2·6H2O and their dehydration products were conducted. The results indicate two possible hydration sequences from lower hydrates to deliquescence at 298.15 K: (1) Hydration of the monohydrate to the dihydrate, followed by the formation of β-CaCl2·4H2O, ending with its deliquescence at 18.5 % RH; (2) Hydration of the monohydrate to the dihydrate, followed by the formation of α-CaCl2·4H2O and of the hexahydrate, ending with its deliquescence at 29 % RH. It was observed that the transition from pure dihydrate to β-CaCl2·4H2O occurs spontaneously, instead of hydration to the thermodynamically stable α-CaCl2·4H2O. The latter phase is only formed in the presence of crystal seeds of α-CaCl2·4H2O that remained after dehydration. Additionally, direct deliquescence of β-CaCl2·4H2O and thus absence of hydration to hexahydrate at 298.15 K is reported for the first time, which could be explained by the more similar lattice structure of CaCl2·2H2O (orthorhombic) and β-CaCl2·4H2O (monoclinic) than α-CaCl2·4H2O (triclinic). Apart from that, an explanation for the observed transformation sequence is proposed, considering the impact of the enhanced solubility of β-CaCl2·4H2O compared to the α-CaCl2·4H2O. The resulting water to salt ratio below six may contribute to the absence of CaCl2·6H2O formation. A Raman spectrum of CaCl2·H2O not reported previously is also provided.

Effect of swirl number on structural and emission characteristics of moderate size burner flames

An experimental investigation was conducted on the flame characteristics, thermal properties, and emissions of industrial-scale (40 kW) diesel flames. This study uniquely measured structural (flame length, diameter, volume), thermal (temperature, heat flux), and emission characteristics (COX, NOX, Unburnt HC) across various equivalence ratios (0.2–0.6) and swirling conditions (Swirl number varying from 0.73 to 1.04). Results indicated that higher swirl numbers generally led to more stable and compact flames, though structural properties were less affected during lean combustion. Notably, flame temperature varied significantly, up to 350 K, resulting in substantial difference in specified emission products. The emission reductions were achieved, particularly with a geometric swirl number of 1.04, which balanced flame performance and emission both, achieving reductions in CO (9–97 %), HC (18–96 %), NO (8–16 %), and Smoke (0.3–82 %). These findings underscore the critical impact of swirl angle on combustion efficiency and emissions, highlighting the need for meticulous optimization to mitigate harmful emissions effectively. This study provides new insights into optimizing combustion parameters for enhanced environmental sustainability in industrial processes.

Incipient fault detection based on dense feature ensemble net

With modern industrial processes becoming more and more complex, the occurrence of faults may cause unmitigated disaster. Therefore, incipient fault detection is very important and has attracted increasing attention Recently, a feature ensemble net (FENet) has been proposed based on a deep feature ensemble framework, which can detect partial notorious faults (such as faults 3, 9 and 15 in the Tennessee Eastman process (TEP)). However, the feature transformer layers in FENet can only mine the process information from the upper layer’s output rather than all features from previous layers. Therefore, a novel dense feature ensemble net (DenseFENet) is proposed to deeply extract the features of data for incipient fault detection in industrial process. DenseFENet firstly constructs base detectors with capabilities for extracting nonlinear features, such as one-class support vector machine, isolation forest, one-class back propagation neural network, one-class long short term memory network and one-class temporal convolutional network, etc. Then, a dense net structure with short paths between non-adjacent feature transformer layers is developed, improving the reutilization capability for shallow knowledge. In addition, a numerical simulation and TEP are adopted to demonstrate the performance of the proposed method. The fault detection rate of DenseFENet has improved by 5.05% and 3.27% correspondingly, revealing the superiority of this approach.

Effect of thermal processing on the degradation of pesticides in a banana jam partially formulated with banana peel flour

Bananas are one of the most widely consumed fruits in Brazil and across the world. However, the intensive use of pesticides in these and other crops can negatively impact human and animal health due to the possibility of pesticide residues persisting in derived products, even after industrial processing. Therefore, this study evaluated the effect of thermal processing on the degradation of the pesticides azoxystrobin, bifenthrin, difenoconazole, and simazine in samples of caramelized banana jam added of banana peel flour (4% w/w) in partial replacement of the fruit pulp. QuEChERS (quick, easy, cheap, effective, rugged, and safe) method and gas chromatography coupled to mass spectrometry (GC-MS) wa used to analyze the above compounds. The method was validated following the standard procedures of the European Commission and ANVISA. Samples of banana pulp were spiked with the pesticides at different concentrations (0.1, 0.5 and 1.0 mg.Kg-1) in order to observe their degradation following thermal processing. Degradation percentages ranged between 28 and 60%, and these were potentially influenced by the physicochemical properties of each compound, as well as the characteristics of the food matrix. The thermal processing provided partial degradations of pesticide residues, some at levels below the MRLs (Maximum Residual Levels) established for bananas. Currently, there is no specific legislation in Brazil and in many other countries for controlling pesticides in processed foods, such as fruit jellies. Therefore, this research highlights the need for the creation of new food laws by government agencies to this end to ensure the provision of safe food to the wide population.

Comparative effects of ammonium nitrogen on perchlorate degradation performance under heterotrophic condition with different carbon sources

Perchlorate (ClO4-) mainly exists in the form of ammonium perchlorate in industrial production. However, the degradation mechanisms of different concentrations of ammonium nitrogen (NH4+-N) and ClO4- mixed pollutants in the environment are not well understood. This study aims to explore the potential of different types of carbon sources for ClO4- and NH4+-N biodegradation. Experimental results showed that the concentration and type of carbon sources are decisive to simultaneous removal of NH4+-N and ClO4-. Under condition of C(COD)/C(ClO4-) ratio of 21.15±4.40, the simultaneously removal efficiency of ClO4- and NH4+-N in acetate (Ace) was relatively higher than that in methanol (Met). C(NH4+-N)/C(ClO4-) ratio of 9.66±0.51 and C(COD)/C(ClO4-) ratio of 2.51±0.87 promoted ClO4- reduction in glucose-C (Glu-C). However, high concentration of Glu could cause pH decrease (from 7.57 to 4.59), thereby inhibiting ClO4- reduction. High-throughput sequencing results indicated that Proteobacteria and Bacteroidetes have made a major contribution to the simultaneous removal of NH4+-N and ClO4-. They are two representative bacterial phyla for participating in both ClO4- reduction and denitrification. Notably, the abundance of main ClO4- degrading bacteria (such as Proteobacteria, Chloroflexi, and Firmicutes) significantly increased by 528.57% in Glu-C. It can be inferred that the concentration of carbon source and NH4+-N were the most important factors determining the removal efficiency of ClO4- by influencing changes in the core microbial community. This study will provide new techniques and mechanistic insights for the simultaneous removal of mixed ClO4- and nitrogen pollutants, which can also provide theoretical support for innovation in future biological treatment processes. Environmental ImplicationPerchlorate is an emerging contaminant whose toxic effect is to impede the absorption of iodine in humans, causing neurological disorders. The pollutant is detected in the natural environment, in daily life and even in food. Ammonium perchlorate is one of the commonly used raw materials in industrial processes. This study focused on the mixed pollution of perchlorate and ammonium nitrogen, and attempted to achieve the degradation of mixed pollutants through carbon sources commonly used in denitrification, and explored the degradation mechanism of mixed pollutants, which provided a reference for solving the degradation problem of mixed pollutants in the environment.

Integration of statistical methods and neural networks for temperature regulation parameter optimization

Temperature control plays a crucial role in various industrial processes, ensuring optimal performance and product quality. The conventional approach to optimizing temperature controller parameters involves manual tuning, which can be time-consuming, labor-intensive, and often lacks precision. This paper introduces an innovative methodology for optimizing the parameters of a temperature controller by integrating statistical methods in the preparation of the experimental plan utilized by neural networks. The integration of statistical techniques in designing the experimental framework enhances the efficiency of data collection, providing a robust foundation for subsequent analysis. The neural network leverages this well-structured dataset to model and optimize the temperature controller parameters, resulting in improved precision and performance. The synergistic integration of statistical methods and neural networks not only streamlines the optimization process but also enhances the reliability of the temperature control system. The effectiveness of the proposed approach is demonstrated through case studies on the Procon level/flow and temperature 38-003 process. The results show significant improvements in temperature control performance, with reduced process variability and faster response times.

The creeping movement of a soft colloidal particle normal to a planar interface

A methodological blend of analytical and numerical strategies employing collocation techniques is presented to investigate the task of describing the Stokes flow generated by a soft particle (composite sphere) moving perpendicularly to a planar interface of infinite extent, separating two semi-infinite, immiscible viscous fluid domains. The particle consists of a solid core enclosed by a porous membrane allowing fluid passage. The movement of the soft nanoparticle has been examined through a continuum mathematical model. This model incorporates the Stokes and Brinkman equations, accounting for the hydrodynamic fields both outside and within the porous membrane layer, respectively. The motion is investigated under conditions characterized by low Reynolds and capillary numbers, where the interface experiences negligible deformation. The solution combines cylindrical and spherical fundamental solutions via superposition. Initially, the boundary conditions at the fluid–fluid interface are satisfied utilizing Fourier–Bessel transforms, subsequently addressing the conditions at the soft particle's surface through a collocation method. The normalized drag force exerted on the particle is accurately calculated, exhibiting robust convergence across various geometric and physical parameters. These findings are effectively visualized via graphs and tables. We juxtapose our drag force coefficient results with established literature data, particularly focusing on the extreme cases. The findings highlight the substantial impact of the interface on the drag force coefficient. Across the full range of viscosity ratios, the normalized drag force decreases as the relative thickness of the porous layer increases. These results enhance the understanding of practical systems and industrial processes such as sedimentation, flotation, electrophoresis, and agglomeration.

Numerical investigation on solids settling in a non-Newtonian slurry inside a horizontal flume

Slurry transportation is always crucial for many industrial processes. This study numerically investigates the settling behavior of multisize solid particles in a non-Newtonian slurry inside a semicircular open channel (flume). The non-Newtonian slurry is modeled using a three-dimensional unsteady Eulerian–Eulerian (E–E) model coupled with the Hershel–Bulkley rheological model. A detailed sensitivity analysis of drag models is performed to establish the solid–fluid interaction in the slurry flow. The numerical model is validated with the experimental data from the literature and shows a fair agreement. The validated model is used to simulate the settling behavior of the slurry in the flume. The mean particle diameter of the solid particles in the slurry is in the range of 75–296 μm with a median diameter of 188 μm. The effect of particle size distributions (PSDs), flume inclination, bubble size, and bubble volume fraction on the particle settling inside the flume is investigated in the parametric study. The analysis of our results revealed that the settling of solids is significantly affected by PSDs in the open channel system. In particular, the increase in flume inclination progresses the settling and dissipation of fine and coarse particles, respectively. Additional simulations showed that the inception of bubbles influences the settling velocity of solids, which changes the settling behavior of multisize solids inside the flume. The presented study can be used as a valuable guideline for the optimization of intermediate exclusion of water from thickened slurry in order to ensure the stability of tailing storage facility.

Viscosity prediction for dense suspensions of non-spherical particles based on CFD-DEM simulations

Reliable estimation of the viscosity and rheology of dense suspensions formed from non-spherical particles is of high importance for studies of many natural and industrial processes. Still, the complexity of underlying physics makes predicting the viscosity of such suspensions a challenging task, resulting in a lack of models capable of doing so for general suspensions. In this work, we present an approach based on a combination of the computational fluid dynamics (CFD) and discrete element method (DEM) developed for arbitrarily shaped particles and use it to predict viscosity of dense suspensions of spheres, rods, and glitters. Simulation results are compared to available experimental data and commonly used engineering correlations. The developed model can reliably predict suspension viscosity in a wide range of solid volume fractions and particle shapes. The simulations with spherical particles also reveal a shear-thickening trend at increased shear rates, which corresponds to the experimentally observed non-Newtonian behavior.