Supercritical water gasification (SCWG) is a highly promising technology. A fundamental aspect of SCWG involves the flow of supercritical water (SCW) around interactive particles, which is inherently complex due to the presence of the wake effect. This study numerically investigates particle wake characteristics and wake-particle interactions in high-viscosity supercritical water (SCW) via an adaptive lattice Boltzmann method (LBM, N/D = 30, coarse-fine ratio 0.025:0.060) to support supercritical water gasification (SCWG) reactor optimization. The adaptive LBM effectively balances accuracy and efficiency, resolving SCW's steep viscosity gradients and fine wake structures well. Interparticle distance (L/D) is the dominant factor for particle drag, affecting trailing particles far more significantly, with three interaction regimes (strong: L/D = 0-2, moderate: 2-4, weak: ≥4). SCW's high viscosity amplifies wake overlap at L/D ≤ 2, minimizing trailing particle pressure drag and suppressing vortex shedding; increasing L/D weakens shielding, elevates drag, and makes trailing particles behave like isolated ones. Interparticle angle raises drag ratios, inducing distinct vortex structures at 30°-60° and 60°-90°, with identical drag at 90°. SCW wake symmetry and vortex shedding show Re-dependent transitions, with critical Re = 92 corresponding to the minimum trailing particle drag ratio. A drag ratio correlation with L/D and Re is also established. This work provides a reliable numerical tool for SCW particle interactions and theoretical guidance for SCWG reactor optimization, with future work focusing on particle swarms and experimental validation.

Shear-induced particle migration in the internal die flows of concentrated particulate suspensions during slot coating processes was investigated using a diffusive flux model implemented within three-dimensional computational fluid dynamics simulations. Comparative simulations demonstrated that particle migration markedly altered the internal flow characteristics and particle distributions inside the slot dies. Inhomogeneous particle distributions at the feed inlet were found to modify the flow behavior in the chamber region and profoundly affect the velocity and particle concentration fields in the slit region. These changes critically influenced the die exit velocity and concentration profiles, which governed the uniformity of the wet coating thickness. The insights obtained from this study offer practical guidance for die design and process control to achieve a uniform coating thickness and stable slot coating operation.

Hydrogen stands as a pivotal energy carrier with significant potential to facilitate the global transition towards a low-carbon energy future. The main challenge in current stage is the production of hydrogen with less emission and high efficiency. To resolve this problem, chemical looping hydrogen production (CLHP) is proposed and investigated. The key factors influencing the efficiency of CLHP are the gas-solid flow, heat transfer and mass transfer processes. A dual-reactor system is established in this work and numerical model based on CPFD is utilised for investigating the reaction characteristics. Hydrogen production decreases with increasing gas velocity in the hydrogen production reactor. Variations in the solid circulation rate alter the mass and heat distribution within the system, consequently affecting the hydrogen production rate. A riser gas velocity of 7 m/s and a circulation rate of 0.15 kg/s are the recommended values for the current constructor. Overall, reactor temperature remains the predominant influencing factor. This study provides insights for reactor optimization and scale-up design.

Numerical simulations were conducted to study the pyrolysis of polypropylene (PP) in a fluidized bed reactor (FBR). For that purpose, a Eulerian-Lagrangian solver was developed, incorporating the gas-solid hydrodynamics in FBR, particle-level heat transfer, and a five-lump pyrolysis reaction kinetic model. This framework captures the mutual interplay among these physicochemical processes and enables predicting the yields of permanent gas (G), light fraction (LF), and heavy fraction (HF). Analysis of the characteristic timescales confirms that the pyrolysis reaction is significantly slower than convective heat transfer. At 505 ℃, LF was the dominant product (67.4 wt%), followed by G (29.6 wt%) and HF (3 wt%), and the product distribution significantly shifted toward G formation with increasing reactor temperature. In contrast, variations in particle size (1.5-2.5 mm) and operation mode (batch-wise vs. continuous) affected the transient thermal behavior but had minor effects on product yields, as heat transfer is not rate-determining under the investigated conditions.

To enhance the calibration efficiency and accuracy of Discrete Element Method (DEM) parameters for cohesive bulk materials, a collaborative method integrating Particle Swarm Optimization (PSO) and Backpropagation (BP) neural networks is proposed. Key macroscopic indicators (steady-state shear stress, angle of repose) are obtained via Jenike shear and funnel tests across a 0-50% moisture range. Orthogonal experiments determine micro-parameters (e.g., static/rolling friction, surface energy) to build a macro-micro mapping database. The core of the PSO-BP dual-model lies in its collaborative mechanism: the forward BP model predicts macroscopic responses to replace time-consuming DEM simulations, while the PSO algorithm optimizes the inverse BP model to accurately infer optimal micro-parameters from experimental macro-indicators (steady-state shear stress, angle of repose). Validation shows low errors (1.14% for angle of repose, 1.63% for steady-state shear stress) and good chute flow velocity agreement. This method overcomes traditional limitations of arbitrariness and ignored parameter coupling, providing reliable support for DEM simulation and equipment design for cohesive bulk materials.

Char particles from coal pyrolysis exhibit unique settling dynamics, such as terminal velocity and drag coefficient. Accurate quantification of drag characteristics is essential for pneumatic conveying. Current simulations adopting spherical drag models suffer large deviations for irregular char particles. To fill this gap, we built a visual platform to investigate free settling of coal and char particles (<200 μm). Terminal velocities were measured for individual particles and aggregates. Based on statistical analysis, we revised Stokes and Schiller drag formulations. In the Stokes form, the fitted coefficients for individual coal particles and coal aggregates are 26.04 ± 3.5 and 52.15 ± 4.8, respectively; for individual char particles and char aggregates, they are 41.05 ± 3.2 and 112.4 ± 4.3, respectively. In the Schiller form, the fitted coefficients for individual coal particles and coal aggregates are 26.19 ± 3.4 and 53.88 ± 4.2, respectively; for individual char particles and char aggregates, they are 40.94 ± 3.2 and 111.1 ± 4.2, respectively. These correlations can be directly employed in drag model for numerical simulation of char pneumatic conveying. Validation shows significantly improved accuracy. This work presents the first experimentally derived drag coefficient correlations for char particles, addressing the lack of quantitative data and offering a more precise tool for char transport design.

To investigate the release of dust, coarse (x_50.3 = 980 μm) and fine (x_50.3 = 3.6 μm) limestone was discharged into a wind tunnel using a model conveyor belt. Different test setups were used to measure the dust release in different phases of the discharge. The discharge mass flow, belt speed and wind tunnel air velocity were varied. Systematic differences were found between the two size fractions. The dust release of the fine material mainly takes place during the fall, while the dust release of the coarse material is dominated by the impact on the bulk pile. For the coarse material, a model for the individual release of dust-laden coarse material particles from the literature can be used as an explanatory approach. A different explanatory approach is required to describe the release of dust from the fine material.

Hydrogel desorption shows promise for thermal management and passive cooling engineering applications, but challenges persist in understanding particle desorption and shrinkage. A Lattice Boltzmann-based phase-field model was developed by combining experiments and mesoscopic simulations to simulate conjugate heat transfer, desorption kinetics, and shrinkage of single hydrogel particles, achieving <5% deviation from experiments. Results show that raising temperature from 343 to 353 K increases 1-h desorption mass by ~12% of initial mass (vs. 1.3% for 313-323 K), driven by rising saturation vapor pressure and falling evaporation enthalpy. Shrinkage is more sensitive at high temperature and low humidity, with surface area reductions of ~17% (343-353 K) and~13% (20-30% RH), compared to ~5% under mild conditions. Reducing particle initial mass from 5.75 g to 0.16 g shortens the time to release 50% of the initial water from 516 min to 117 min, indicating greater efficiency for smaller particles. This work bridges the macro-pore scale gap and offers a numerical tool with design insights for passive cooling.

Sorption-enhanced steam gasification (SESG) represents a promising route for hydrogen-rich syngas production. However, the rapid deactivation of conventional CaO-based sorbents, and the efficiency loss associated with the high temperature sorbent regeneration step, remain as critical challenges. In this study, a redox-activated SrMnO₃ sorbent was used for isothermal SESG of biomass in a fluidized-bed to investigate fuel flexibility, torrefaction effects, and long-term stability. Four biomass feedstocks as well as their torrefied counterparts were evaluated. All untreated feedstocks produced hydrogen-rich syngas with H₂ concentration >60% and H₂/CO ratios >4 under isothermal operation at 850 ℃. Torrefaction decreased H₂ purity and syngas yield due to reduced volatile matter content. Long-term experiments in both a fluidized-bed reactor and TGA demonstrated excellent cyclic stability of SrMnO₃ and strong ash resistance. The structural and compositional properties of the sorbents were characterized in detail, confirming the chemical and structural stability of the SrMnO₃ sorbent during prolonged cyclic operation. Process simulation further showed that SESG of biomass significantly enhanced cold gas efficiency while maintaining a comparable heat demand compared to state-of-the-art indirect biomass gasification. Overall, SrMnO₃ exhibits strong potential as a robust sorbent for efficient and flexible biomass-to-hydrogen conversion.

Chemical recycling of polymethyl methacrylate (PMMA) to its monomer, methyl methacrylate (MMA), requires balancing primary depolymerization with the suppression of secondary gas-phase reactions. This study investigates non-oxidative MMA decomposition in a fluidized bed reactor across a temperature range of 623 to 1073 K using online FTIR spectroscopy. Experimental results reveal a significant shift in product selectivity: low temperatures favor a low-energy decarboxylation pathway (yielding CO₂ and methanol), while high temperatures promote radical cracking (yielding CO and light hydrocarbons). To describe this, a two-competing-reactions model (CRM) is used, outperforming the traditional single first-order approaches. The CRM identifies two distinct activation energies: E_a,1 = 76.5 kJ mol⁻¹ for decarboxylation and E_a,2 = 269.9 kJ mol⁻¹ for cracking. The research further demonstrates that the classical sequential decomposition model (PMMA → MMA→ light gases) overpredicts monomer yields at low temperatures. By integrating a direct solid-to-gas pathway to account for side-chain break-off and incorporating multi-volume reactor hydrodynamics, the model's predictive accuracy significantly improved. This integrated framework identifies an optimal recovery window near 723 K, achieving MMA yields over 95 %.