The uniformity of the wet-particle cloudy zone in liquid-sprayed fluidized beds directly governs coating quality, yet the chaotic nature of gas–solid multiphase flow makes its active control extremely challenging. This work first establishes a closed-loop control framework coupling computational fluid dynamics with the discrete element method (CFD-DEM) and deep reinforcement learning (DRL), in which the multiphase flow simulator serves as the environment and a Soft Actor-Critic (SAC) agent acts as the controller to adjust three independent inlet fluidization velocities in real time. To overcome the computational bottleneck of multiphase flow simulation, dream training is introduced for the first time into active control of multiphase flow reactors: an ensemble surrogate dynamics model is constructed from CFD-DEM data, enabling the agent to explore control strategies entirely within the surrogate and improving the training efficiency by approximately four orders of magnitude. Leveraging this capability, multiple reward function designs are systematically investigated, revealing that the agent autonomously discovers a differentiated multi-inlet pulsed fluidization strategy. We also introduce the concept of meso-intelligence, which embeds mesoscale structural awareness into the reward design, and further propose a macro-meso dual layer reward function structure to enhance the spatial uniformity of the cloudy zone. This work demonstrates that replacing multiphase flow simulation with a surrogate model for policy training enables systematic reward exploration and policy convergence at negligible computational cost, offering a new paradigm for active flow control in multiphase reactors.

Multiphase flow dynamics critically governs mass transfer, reaction kinetics, and overall system efficiency in alkaline water electrolyzers (AWEs). To address the inherent flow maldistribution in large-scale cells, this work proposes a gradient-distributed concave-convex plate, featuring elements densely packed at the center and sparsely distributed at the periphery. A pilot-scale transparent electrolyzer (380 mm diameter) is developed to directly observe gas-liquid flow patterns, enabling comparative analysis between the proposed gradient structure and a conventional uniform design. The experiments demonstrate that the novel design significantly improves electrolysis performance, enhancing efficiency by 3.0% at 2 L/min and 4.4% at 3 L/min under a current density of 4000 A/m2. The gradient structure also achieves superior gas holdup control, with a 26% reduction at 5 A and a 15% average reduction across the investigated range compared to conventional plates. As electricity dominates AWE operating costs, this gain offers a low-cost path to reducing hydrogen production costs.

Scale-up effects on the hydrodynamics in bubble columns are particularly critical for reactor design and optimization. However, unlike empty bubble columns, there remains a lack of comprehensive understanding of scale-up effects in the presence of vertical internals. To address the gap, this study investigated the effects of column scale on gas holdup, flow field and turbulence properties in the bubble columns with tube bundle internals at superficial gas velocity 0.12 m/s through CFD simulation. The predictions indicated that, the radial distribution of gas holdup becomes more uniform with scale-up. Moreover, as the column scale increases, the flow pattern in the empty columns consistently exhibits typical gulf-stream pattern. However, for the bubble columns with internals, the flow pattern shifts from the gulf-stream to a dual-circulation pattern with the flow reversal in the center. The effects of column scale on the turbulence properties are also completely different for the empty column and the column with internals. Mechanism analysis demonstrated that the gas holdup distribution governs the large-scale liquid circulation pattern, while turbulence viscosity plays a pivotal role in regulating circulation intensity. We believe that these findings could provide more insight for the design and scale-up of bubble column reactors with internals.

Achieving uniform liquid film distribution among multiple particles during droplet coating is critical yet challenging due to the nonlinear and stochastic nature of multiphase flows. This study proposes a closed-loop framework that synergizes Volume-of-Fluid (VOF) simulations with deep reinforcement learning (DRL) for intelligent coating control. Within this CFD-DRL framework, a Distributional Soft Actor-Critic (DSAC) agent perceives real-time flow fields via CFD probe arrays and continuously adjusts injection velocity and droplet diameter. The agent autonomously uncovers mesoscale games governing uniform droplet coating, discovering a synergistic-antagonistic coordination between velocity and diameter that stabilizes the Weber number within an optimal range. This significantly enhances inter-particle coating uniformity while maintaining high singleparticle coverage. The algorithm exhibits strong adaptability to geometric constraints, spontaneously switching strategies between dispersed and compact particle arrangements. Crucially, analysis of these mesoscale games reveals a form of emergent mesoscale intelligence: the agent achieves global statistical uniformity through temporal-domain alternating bias, demonstrating that local non-uniformity at the mesoscale is the necessary route to global uniformity. This work establishes a new paradigm for intelligent control in complex multiphase processes.

Droplet size and its distribution are critical determinants of polystyrene (PS) properties in suspension polymerization, yet the specific roles of inorganic dispersants and surfactants remain insufficiently understood. In this study, a non-reactive styrene-water system was employed to study the coupling effects between tricalcium phosphate (TCP) and sodium dodecyl benzene sulfonate (SDBS). Interfacial tension measurements, combined with Szyszkowski-Langmuir analysis, provided quantitative insights into surfactant adsorption, while a systematic investigation of the effects of dispersant concentration, surfactant concentration, agitation speed, and oil-to-water ratio on droplet size revealed the roles of these factors in controlling droplet size and emulsion stability. Incorporating the relevant mechanisms of TCP and SDBS into the classical empirical correlation effectively captures their interaction and accurately predicts the Sauter mean diameter. This result enhances our understanding of the complex interactions among inorganic particles, surfactants, and hydrodynamic conditions in suspension polymerization, and provide quantitative guidance for droplet size control and the optimization of industrial polystyrene synthesis.

As a vital pathway for decarbonizing the steel industry, hydrogen-based fluidized-bed ironmaking offers high efficiency by directly utilizing iron ore fines. However, the inherently large specific surface area of fines makes them exceptionally susceptible to adhesion-induced defluidization. Simulating defluidization is traditionally hindered by the extreme timescale mismatch between hour-scale reduction kinetics and millisecond-scale granular hydrodynamics. The present work establishes a practical computational fluid dynamic-discrete element method (CFD-DEM) coupling framework, decoupling reduction from hydrodynamics via a state-mapping strategy. To capture the intrinsic nonlinear acceleration of solid-state diffusion, the conventional sintering force model is modified by introducing a sigmoidal temperature correction. Crucially, the framework is quantitatively validated against high-temperature experiments, accurately predicting the critical iron content triggering defluidization across 873-1073 K and resolving the severe overestimations inherent in the conventional sintering force model. Comprehensive particle-scale analyses, encompassing interparticle force competition, contact duration statistics, and transient flow structures, reveal the micromechanics of agglomeration. We elucidate that defluidization is governed by the synergistic coupling of temperature and iron content: temperature acts as a temporal accelerator compressing the characteristic sintering timescale, while iron content serves as a spatial determinant providing topological connectivity. This coupled interplay triggers a percolation-driven phase transition, where transient cohesive clusters abruptly merge into a system-spanning rigid skeleton. Building upon the mechanistic understanding, a high-resolution regime map is constructed to delineate the nonlinear operational boundary, establishing a validated engineering blueprint to proactively avert bed defluidization and optimize the industrial operation of hydrogen metallurgy technologies.

Converting CO2 into valuable light olefins via the CO2-Fischer-Tropsch (CO2-FTS) process demands highly efficient, selective, and stable catalysts. This study investigated the promotional effects and underlying mechanism of Zn in Na-modified Fe catalysts, successfully synthesizing a nearly phase-pure ZnFe2O4 spinel precursor. The optimal Fe2Zn1-Na catalyst achieved 36.5 % CO2 conversion, 39.5 % C2-C4 olefin selectivity with a C2-C4 olefin-to-paraffin(O/P) ratio of 6.5 at 320 degrees C, outperforming the Fe-Na catalyst and demonstrating long-term stability with negligible deactivation over 80 h. Characterization revealed that the crucial role of ZnFe2O4 spinel structure in stabilizing the active chi-Fe5C2 phase, enhancing metal dispersion through Fe-Zn interactions, and improving morphological stability by inhibiting sintering. A detailed kinetic model, coupling the reverse water-gas shift (RWGS) formate pathway with FTS mechanisms (carbide, enol, and CO insertion), was developed and validated against experimental data. Kinetic analysis strongly supports the formate-carbide mechanism as the dominant reaction pathway and quantitatively identified FTS steps as rate-limiting for overall hydrocarbon synthesis. Incorporating olefin readsorption terms significantly improved model accuracy, specifically capturing the experimentally observed decrease in the O/P ratio with increasing carbon number. Moreover, the model accurately explains production distribution, attributing the increase in C2-C4 O/P ratio with temperature to a favorable interplay of activation energies and decreased surface hydrogen coverage. This work not only demonstrates the promotional effects of Zn in precise precursor phase control but also provides a kinetic framework that offers quantitative mechanistic understanding and guidance for catalyst design and process optimization for sustainable CO2 to light olefin production.

The emission of CO2 is the primary cause of global warming, and the absorption of CO2 by ammonia solution based on a bubble column reactor is an effective method for carbon capture. This paper utilizes the UDF function in commercial CFD software for numerical simulation and validates the results through experiments. The study investigates the impact of multi-stage baffle parameters (i.e., size, quantity, and position) on the CO2 absorption and ammonia escape processes, while also analyzing the mass transfer characteristics of the bubble column reactor under different operating conditions (i.e., ammonia concentration, inlet CO2 volume fraction, temperature, and pressure). The results indicate that the introduction of baffles promotes CO2 absorption, and the influence of baffle number and position are more significant than size. Increasing the number of bubble generators also enhances absorption, and the ammonia escape process becomes more intense as the removal efficiency increases. Through a comprehensive analysis of the system energy consumption and CO2 absorption enhancement, the optimal baffle configuration for the bubble column reactor consists of four baffles positioned on the left side, each with a length of 6 mm and separated by a spacing of 2 mm. Furthermore, increasing the ammonia concentration and temperature can enhance the CO2 removal efficiency and the overall mass transfer coefficient of ammonia escape, while an increase in the inlet CO2 volume fraction has the opposite effect. Although an increase in pressure promotes removal efficiency, it reduces the overall mass transfer coefficients of both CO2 and ammonia escape.

Bubble-induced turbulence (BIT) plays a crucial role in the computational fluid dynamics (CFD) simulation of bubbly flows, yet its precise effects remain an open question. This study investigates the influence of BIT modeling on the predicted hydrodynamics in two bubble columns of different diameters (14 cm and 44 cm). Our results indicate that while incorporating BIT significantly improves predictions for the larger column, it offers minimal advantage for the smaller one. Specifically, BIT modeling markedly accelerates the flow development in the 44 cm column, whereas in the 14 cm column, the flow already develops rapidly even without BIT, rendering its effect comparatively weak. To elucidate the underlying mechanism, we analyze the radial distribution of the BIT source term Sk. In the large-diameter column, Sk remains relatively flat across the core region before declining sharply near the wall. In contrast, for the small-diameter column, Sk begins to decrease rapidly from the center, suggesting a fundamentally limited influence of BIT. This study highlights the scale-dependent nature of BIT and offers practical insights for model selection in bubble column simulations.

Quantifying surfactant effects in bubble columns remains challenging. The difficulty lies in linking local interfacial phenomena, such as bubble deformation and liquid-film drainage, to macro-scale hydrodynamics. In our previous study (Liu et al., AIChE J. 2025; 71 (10):e18902), the Marrucci number (Ma)-the ratio of resistance induced by surface tension gradients to the driving force during liquid film drainage-was identified as a key parameter for quantifying surfactant effects, and a critical velocity correlation was developed based on bubble coalescence experiments. In this work, the critical velocity model was incorporated into the coalescence kernel within the PBM framework, and the CFD-PBM simulations reproduced the Sauter mean diameter () observed in experiments for various ethanol concentrations. Additional simulations in propanol solutions further demonstrate the model's applicability by capturing the distinct interfacial effects of different surfactants. These findings show the model's potential to reflect surfactant effects in macro-scale fluid dynamics.

Major iron and steel companies have identified the H2-based shaft furnace (SF) as a key decarbonization technology for future development. Its continued advancement aims to achieve full H2 operation and broaden the applicability of ores with different grades. However, published studies report contradictory trends in SF reduction performance with increasing H2 content, leading to significant confusion about the role of H2. The underlying causes remain unclear, and the influence of ore properties on SF performance has received limited investigation. In this work, using our recently developed CFD model for industrial SFs, the influence of ore properties on SF reduction behavior is investigated under various H2 contents. Differences in ore properties focus on thermodynamic equilibrium differences, as documented in two well-known databases, NIST and FactSage. The results show that thermodynamic equilibrium differences can modify the contributions of high- and low-temperature reduction, leading to inconsistent trends in metallization as H2 content increases. The gas flow rate is also a contributing factor, as it alters the H2:CO range in which the thermodynamic disadvantage of H2 reduction hinders reduction. These findings provide insight into how ore properties and the reducing gas flow rate modulate in-furnace H2 reduction and highlight the need for reliable ore characterization to properly assess H2-based SF performance.

As a promising low-carbon ironmaking technology, shaft furnace (SF) direct reduction using pure H2 is attracting significant attention from the steel industry. However, how SF performance evolves during the transition from H2-rich to pure H2 operation remains insufficiently understood, and how metallization under high H2 conditions can be improved remains unclear. A recently developed process model, further developed and validated to include both high- and low-temperature reduction steps, is applied to address these gaps. The comparative analysis highlights the importance of low-temperature reduction in the overall reduction process under high-H2 conditions. It also provides insight into the decline in reduction performance at a higher H2 content. Subsequently, systematic studies of gas-injection strategies are conducted to cover the effects of reducing gas temperature and flow rate, as well as N2 addition methods. The results identify feasible operational routes under pure H2 conditions, significantly increasing metallization while maintaining appreciable H2 utilization.

The shaft furnace is a promising low-carbon ironmaking technology, but its complex internal processes pose challenges for efficient operation and control. Numerical simulation provides an effective method for revealing the internal states, whereas many existing models incorporate simplifications that limit prediction accuracy. This study presents a CFD model for industrial MIDREX shaft furnaces, incorporating major reactions, including CH4-related side reactions. An improved unreacted shrinking core model (USCM) is introduced for reduction based on the thermodynamic equilibrium diagram. It allows for the decoupling of reaction and diffusion layers, reducing the reaction layers and enabling variable diffusion structures. Consequently, the reaction model provides a more physically realistic and thermodynamically consistent representation of the multi-step reduction process, is valid for H-2-CO mixtures, and increases computational efficiency by 65-75 %. The model is validated using four sets of laboratory and industrial data, covering inner states and overall performance parameters. It also successfully captures the influence of CH4, an inevitable component arising from reforming. A high CH4 content causes substantial heat loss near the gas inlet and reduces metallization, primarily due to the combined effects of endothermic reduction and side reactions. Based on simulation results, an optimal CH4 level is identified to balance metallization, gas utilization, and energy efficiency. The model can serve as a valuable tool for analyzing shaft furnace performance and has the potential to support the evaluation of advanced decarbonization strategies such as high H-2 content operation.

Abstract The spreading and evaporation of liquid droplets on hot particle surfaces are critical to liquid‐sprayed fluidized bed design. While coarse‐grid approaches (CFD‐DEM, Euler–Euler) are commonly used in reactor‐scale simulations, they lack resolution of microscale interfacial phenomena at the single particle level. This work performs high‐fidelity Volume‐of‐Fluid (VOF) simulations coupled with an interface evaporation model to investigate droplet impact and evaporation on heated spherical particles over wall temperature of 250°C–400°C and Weber number of 8–84. Results show that total evaporation time is insensitive to particle temperature, while droplet morphology and evaporated mass strongly depend on wall temperature and Weber number. Moreover, an effective evaporation area coefficient derived from VOF simulations is introduced as a correction factor for macroscopic models, enabling coarse‐grid simulations to retain essential microscale physics without explicitly resolving interfaces. It is expected to improve predictive accuracy for simulation of liquid‐sprayed fluidized bed with coarse‐grid approaches.

Rotating disk contactors (RDCs) have been commonly used for extraction due to their simple design and high throughout. However, accurate prediction of phase holdup remains difficult in view of the complex multiphase flow characteristics in RDCs. Drag models plays a crucial role as they directly determine both global and local predictions of phase holdup in RDCs. In this study, we present a new drag model developed based on the EnergyMinimization Multi-Scale (EMMS) approach. The proposed EMMS-based drag model represents a significant advancement as it eliminates the need for adjustable parameters, thereby enhancing the accuracy of phase holdup prediction compared to existing models that often rely on specified droplet diameter. Furthermore, phase inversion phenomenon was analyzed based on the predicted flow pattern, indicating that up to 50% of oil can transition into a continuous phase under high rotational speed. The new model offers a more comprehensive understanding of the complex multiphase flow behaviors in RDCs, and valuable guidance for the design and optimization of liquid-liquid extraction processes.

In liquid‐injected fluidized bed processes, such as ethylene polymerization, it is crucial to quantitatively identify the cloudy zone consisting of gas bubbles, droplets, and wet particles. While various experimental methods exist for measuring relevant parameters, a comprehensive understanding of the characteristics of the cloudy zone remains challenging. This study introduces a particle‐based method to identify the cloudy zone using CFD–DEM simulations, focusing on heat and mass transfer during liquid evaporation and particle‐droplet collisions. Image analysis of simulation results reveals a horseshoe‐shaped cloudy zone and elucidates the life cycle of wet particles, transitioning from dry to wet and back to dry. The investigation identifies five distinct stages characterized by changes in evaporation rate, temperature, and liquid mass during the core‐annular flow of wet particles. Importantly, the temperature gap between dry and wet particles diminishes as thermal energy transfer during droplet‐particle collisions weakens at higher liquid injection rates.

Gas-insulated switchgears and lines (GISs/GILs) are critical components in world engineering 'west-to-east power transmission' infrastructure and form the physical backbone of the China' s new national energy strategy. However, operational failures have revealed that friction-induced micro/nanometallic particles from internal components can accumulate and interact with the structural weak points of insulators, leading to abnormal surface discharges. These dust-induced discharges differ significantly from those caused by larger foreign objects, and current detection methods remain insufficient to characterize their complex electrodynamic behaviors or predict discharge risks. To address this challenge, a coupled electro-thermal-fluid multiphysics simulation model for GIS/GIL cavities was developed, incorporating microscale force analysis and particle dynamics to track the motion, dispersion, and surface adsorption of charged dust particles. A particle-tracking algorithm and collision adhesion mechanism were proposed to simulate dust uplift, diffusion, and flashover-triggering behaviors near insulators. Comparative experiments were conducted to investigate the effects of key variables such as initial mass, particle size, material, spatial location, and voltage type on discharge pattern. Based on this, a multifactor hazard assessment double-level framework was further established using gray relational analysis and the entropy weight method. The results indicate that particle mass has the highest influence, while voltage polarity has the least. Flashover voltage can decrease by up to 23% due to particle accumulation and explosive dispersal. The simulation outcomes align well with experimental observations, and a dust hazard evaluation system with a threshold of 0.85 was established based on correlation scoring that was experimentally validated. This work provides theoretical guidance for discharge prevention and insulation design under micro/nano-dust contamination in high-voltage GIS/GIL systems.

This study systematically investigates the influence of liquid level height (Vt/VU) on hydrodynamic performance and mixing efficiency in loop bioreactors, a promising technology for energy-efficient single-cell protein production. Through controlled tracer experiments across varied liquid levels (Vt/VU=1/8 to 3) and stirring speeds (1200-1800 rpm), we demonstrate that reduced liquid levels (Vt/VU <= 1/4) induce gas entrainment at high speeds (>= 1600 rpm), increasing flow resistance and reducing mean fluid velocity. Conversely, elevated liquid levels (Vt/VU >= 1) enhance diffusion-driven mixing, shortening mixing time by 2/3 at Vt/VU=3 compared to 1/8. A critical transition from convection-dominated to diffusion-dominated regimes is identified at Vt/VU=1/2, with mixing efficiency becoming less sensitive to stirring speed at Vt/VU >= 2. These findings provide feasible guidelines for optimizing loop scalability and advancing reactor optimization strategies for continuous bioprocessing.

Liquid-liquid dispersion is often performed in stirred tanks, which are valued for their ease of operation, high droplet generation rate and effective droplet dispersion. Many relevant simulations use the Eulerian-Eulerian method, combining population balance equations with statistical models to forecast droplet breakage. Conversely, the Eulerian-Lagrangian (E-L) method provides precise tracking of individual droplets, which is crucial for simulating dispersion processes. However, E-L simulation faces challenges in integrating droplet breakage effectively. To address this issue, our research introduces a probabilistic approach for droplet breakages. It assumes that a longer time increases the likelihood of breakup; a droplet breaks if the calculated probability exceeds a random value from 0 to 1. Consequently, the simulated breakage frequency becomes independent of the Lagrangian time step. The Sauter mean diameter and droplet size distribution can be accurately predicted by this probabilistic approach. By closely monitoring droplet motion, we reveal the complexity of droplet trajectories and the detailed patterns of circulation in stirred tanks. These insights contribute to a deeper understanding of liquid-liquid dispersion dynamics. (c) 2025 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.