Pyrolysis of thermally-thick biomass particles is a highly complex process involving fluid-particle coupling, anisotropic intra-particle transfer, and detailed pyrolysis reactions in a porous particle. To enable numerical predictions under widely varying operating conditions, a comprehensive biomass pyrolysis model using detailed kinetics coupled with an anisotropic porous media model with resolved fluid-particle coupling is developed. The results show that the detailed model can provide reasonable predictions of biomass pyrolysis in a wide range of conditions, including temperatures ranging from 550 to 1800 K, particle size ranges from 3 mm to 25 mm, moisture contents up to 66.67% (dry basis), various particle shapes, different wood species, as well as radically different flow conditions (natural convection and forced convection flow modes). Intra-particle heat transfer and fluid-particle coupling exert substantial effects on the pyrolysis process, while the influence of anisotropic properties is relatively mild. Sensitivity analysis identifies wood thermal capacity, emissivity, and thermal conductivity as the most critical parameters; three out of 32 reaction rates have significant effects on the modelling results at low-temperature conditions, while all reaction rates show a weak impact on the modelling results at high-temperature conditions, due to a large internal thermal Damko & uml;hler number (Da > 10). These findings provide profound theoretical insights into the pyrolysis mechanisms of thermally thick biomass particles, verifying that the model serves as a powerful tool for predicting the pyrolysis behaviors of thermally thick biomass particles, with direct implications for the design and optimization of biomass pyrolysis reactors.

White pollution has intensified due to plastics' inherent degradation resistance. Catalytic pyrolysis can convert waste plastics into valuable chemicals like light olefins and aromatics while reducing pollution. However, insufficient selectivity for target products remains a challenge. In this study, the shaped ZSM-5 (Z5) microspheres from industry was used and modified via 3.0 wt% impregnation of P, Ga, and Zn to enhance the yield of light olefins and aromatics in ex-situ catalytic pyrolysis of polypropylene (PP). Incorporating P, Ga, or Zn modulated catalyst acidity, promoting the dehydrogenation /aromatization reactions and significantly increasing liquid, light olefin and aromatic yields. The Zn-Z5 microspheres gave the highest light olefin yield (36.7 wt%), and decreased alkane yield by 24.6 wt%, sharply increasing olefin-alkane ratio from 0.52 to 1.56, compared with Z5. Concurrently, the yields of toluene, ethylbenzene, xylene and C9 + aromatics in the liquid products were increased, with the yield of aromatics rising from 22.6 wt% (Z5) to 38.4 wt% (Zn-P/Z5). Notably, the p-xylene (PX) selectivity in xylenes significantly improved, with the selectivity of Zn-P/Z5 attaining 75.3 %. These results show that employing P-, Ga-, and Zn-modified Z5 microspheres for the catalytic pyrolysis of plastics facilitates the high-value recycling of waste plastics, achieving co-production of light olefins and aromatics with high PX selectivity.

During plastic catalytic pyrolysis, HZSM-5 faces challenges such as limited mass transfer and low active site utilization, resulting in poor aromatization and anti-deactivation performance. To address these challenges, a hollow HZSM-5 catalyst with a hierarchical micro-meso-macroporous structure was designed in this work. The structure-activity relationship of hollow HZSM-5 was then systematically explored, focusing on the influence of pore structure and acidic sites on catalytic performance. The hollow HZSM-5 catalyst without metal loading exhibited outstanding catalytic performance in the catalytic pyrolysis of plastics, with high selectivity of 95.79% for aromatic hydrocarbons (AHs) and 83.08% for monocyclic aromatic hydrocarbons (MAHs). More importantly, hollow HZSM-5 delivered outstanding stability with an initial performance retention of 95.75% after five cycles and achieved selectivity of AHs exceeding 86% for various polyolefin plastics. The excellent catalytic performance was attributed to the synergistic optimization of pore structure and acidic sites. On the one hand, the hierarchical porous structure significantly enhanced mass transfer, thereby improving the anti-deactivation performance and active site utilization of the catalyst. On the other hand, acidity enhanced by desilication efficiently promoted the aromatization of plastics. This work highlighted the importance of rational catalyst design in achieving efficient and stable catalytic pyrolysis of plastics toward valuable chemical products.

The mechanisms by which calcium-rich inorganics within pulp and paper mill sludge (PPMS) influence the pyrolysis of its organic components remain largely unexplored. This study investigated the effects of inorganic constituents on the evolution of pyrolysis products from major organic components (cellulose, plastics, lipids, proteins, and ink) in PPMS through fixed-bed pyrolysis experiments. The results demonstrated that inorganics significantly promoted secondary tar reactions by improving heat transfer, providing catalytic sites, and prolonging the residence time of pyrolysis vapors. These effects markedly increased gas yields, particularly CO2 (e. g., by 29.27-35.41 mL/g for cellulose), and decreased tar yields (e.g., by 10.27-11.38 wt% for cellulose), while also altering tar composition. Specifically, calcium-rich inorganics catalyzed the conversion of cellulose into small ketone molecules such as hydroxyacetone, reducing the proportions of anhydrosugars and furans. For plastics, PPMS ash promoted the beta-scission of long-chain aliphatic hydrocarbons and the transformation of monocyclic hydrocarbons into polycyclic aromatic hydrocarbons while also increasing char formation. For lipids, calcium-rich inorganics effectively catalyzed the decarboxylative ketonization of palmitic acid to produce 2-heptadecanone. For proteins, PPMS ash suppressed NH3 release and promoted nitrogen migration into nitrogencontaining heterocyclic compounds. For ink, the inherently high ash content limited the regulatory effect of additional ash. Overall, this study provides mechanistic insights into PPMS pyrolysis and supports its sustainable conversion.

The abundance of inherent micropores in biomass-based carbon restricts potassium ion transport, which in turn hinders both adsorption and intercalation kinetics. Increasing mesopore content can significantly enhance potassium ion transport, but quantitative regulation of mesoporous content remains challenging. Furthermore, the mechanism by which mesopore content affects reaction kinetics is not fully understood. In this work, carbon anodes with controlled mesopore content were synthesized by replicating SBA-15 zeolite structures via a coating method. For the first time, the relationship between mesopore content and potassium-ion storage performance is systematically explored. The increase in the mesopore content can both improve the enhance adsorption and intercalation kinetics, thereby improve the discharge capacity. However, excessive mesopores reduce the adsorption ratio, negatively impacting cycling stability. Therefore, an appropriate mesoporous content exhibits the best performance. This study offers a strategy for the regulation of mesopore content in carbon anodes and provides new insights into the role of mesopore content in enhancing potassium ions storage performance.

Nitrogen doping is a widely adopted strategy to enhance the electrochemical performance of biochar anodes for lithium-ion batteries. However, conventional methods predominantly rely on chemical reagents, which are not only costly and environmentally burdensome but also struggle to simultaneously achieve the synergistic optimization of doping and hierarchical porosity, which is essential for high-performance biochar anodes. In this work, we propose a sustainable mixed-biomass strategy that leverages soybean meal as an intrinsic nitrogen source and camellia oleifera shells as a carbon precursor, combined with CO2 activation, to synergistically regulate nitrogen defect chemistry and hierarchical pore evolution. Systematic optimization of the biomass ratio enables simultaneous formation of nitrogen-induced defects and CO2 driven mesopore/macropore reconstruction. The resulting nitrogen-doped biochar (NAC-3) exhibits enlarged interlayer spacing and abundant pyridinic/pyrrolic nitrogen species. Owing to these structural merits, NAC‑3 exhibits a high reversible capacity of 181.2 mAh g⁻¹ at 2.0 A g⁻¹ , demonstrates good cycling stability with 89.3% capacity retention after 200 cycles, and shows rapid Li⁺ diffusion kinetics, as reflected by a low Warburg coefficient (σ = 82.59 Ω·s⁻¹/²). When integrated into a full cell with LiNi0.58Co0.12Mn0.30O₂ cathode, the NAC‑3 anode enables an initial discharge capacity of 196.7 mAh g⁻¹ , which remains at 165.7 mAh g⁻¹ after 200 cycles. This work establishes a green, scalable route for constructing high-performance biochar anodes and reveals the cooperative mechanism between nitrogen doping and pore architecture engineering, providing a valuable reference for the practical development of next-generation lithium-ion batteries.

In this study, we systematically simulated and compared four stepwise reaction models for biomass pyrolysis using the lattice Boltzmann method (LBM). The LBM framework was employed to resolve multiphysics coupling - including heat/mass transfer, fluid dynamics, and reaction kinetics - by solving discretized conservation equations for mass, momentum, and energy across gas-solid phases. This approach enabled particle-scale tracking of temperature evolution, species distribution, and reaction pathways. Results indicate that while all models exhibit comparable trends in temperature fields, mass transfer rates, and product distributions, they diverge significantly in plateau period duration and late-stage temperature dynamics. Model 4 uniquely captured exothermic-driven temperature overshoots due to its explicit integration of intermediate solids and secondary reactions. Solid mass loss rates varied across models, with Model 2 showing the slowest decomposition (28.5% residual char) and Model 4 the fastest (20.2% char). Product yields further highlighted mechanistic differences: Models 1 and 3 favored syngas and tar (combined similar to 80%), while Model 2 optimized char production. This not only highlights the specific applications of each model but also provides valuable insights for understanding the biomass pyrolysis process at particle scale.

The biomass fast pyrolysis in bubbling fluidized beds represents a complex, nonlinear process involving multiscale spatiotemporal evolution. Due to the high computational cost of the traditional discrete element model (DEM) and the widespread use of thermally thin models, single-scale numerical models are no longer able to meet the current simulation needs. A one-dimensional particle-scale model for directly solving internal heat transfer is constructed in this paper, and it is coupled with coarse-grained (CG) CFD-DEM to provide a highprecision, low-cost multi-scale simulation strategy for the fast pyrolysis of large biomass particles. Here, the CG particle model and DEM are used to model the motion of sand and biomass, respectively. The results indicate that the product yield is closer to the experimental value when considering the intra-particle heat conduction, which is independent of the change in CG ratio (k). Compared with traditional CFD-DEM (k = 1), the processing at k = 2 and 3 can reduce wall-clock time by approximately 86.3% and 96.6%, respectively, while retaining highfidelity simulation accuracy. Furthermore, the instantaneous evolution details and thermophysical processes of gas-solid phases in the pyrolysis of large biomass particles can be accurately captured, including the migration of the solid phase in axial and radial directions, bubble evolution, biomass residence characteristics, and distribution of particle size, as well as the intra-particle heat conduction, product formation, etc. Future work will prioritize improving the applicability of multi-scale models, with a focus on integrating GPU parallel acceleration technology and detailed pyrolysis reaction kinetics.

Catalyst particles undergo various physicochemical changes during catalytic processes, significantly affecting reactor performance, conversion, and product yields. These changes, including agglomeration and sintering, attrition and fragmentation, and surface deactivation via coke deposition, can alter particle size distribution, decrease active surface area, and reduce overall reactivity. This review comprehensively assesses common particle-level transformations, their mechanisms, consequences, and discrete element method (DEM) modeling approaches. Emphasis is placed on accurately capturing these effects and on identifying appropriate computational models to simulate them, to support reactor design and process intensification. Challenges associated with current DEM modeling capabilities, including hardware and software limitations, are discussed. Future research directions are highlighted, particularly in the area of electrification for gas-solid reactors. Modeling strategies for resistive, microwave, and inductive heating are proposed. Finally, the role of particle-level changes in enabling data-driven models and digital twins is explored, with emphasis on real-time process prediction and control. This work provides insight into modeling gas-solid catalytic processes and underscores the urgent need for advanced computational models that accurately simulate catalyst transformations.

The fast pyrolysis of pulp and paper mill sludge (PPMS) in a bubbling fluidized bed (BFB) is a promising solution for converting PPMS into liquid fuels or chemicals for waste utilization but lacks sufficient understanding. This study comprehensively explored the effects of bed temperature, fluidization number, and particle size on the fast pyrolysis of deinking sludge (PPMS-DS) and sewage sludge (PPMS-SS) from a wastepaper pulp and paper mill, unveiling for the first time the combustion behavior of PPMS tar, gas release patterns, and reaction kinetics. Results revealed that bed temperature was the most critical factor influencing tar yield, followed by the fluidization number. Under optimal conditions, the tar yields were 61.49 wt% for PPMS-DS and 66.13 wt% (dry ashfree basis) for PPMS-SS. PPMS-DS tar exhibited better fuel properties, with a higher heating value (38.41 MJ/kg) and lower oxygen content (7.08 wt%). The combustion of PPMS tar involved three steps: low-temperature oxidation (LTO), fuel deposition (FD), and high-temperature oxidation (HTO). The kinetic differences among the reaction steps were clear, with LTO and FD having the respective lowest and highest apparent activation energies. This research supports the development of efficient and sustainable PPMS-to-energy technologies, with significant potential for large-scale industrial applications.

Constrained by the prevalent use of thermally-thin particle assumptions and the high computational overhead of the traditional discrete element model (DEM), existing single-scale modeling methods are inadequate for simulating biomass fast pyrolysis. In this work, a one-dimensional particle-scale model for directly resolving intra-particle heat transfer is first developed and subsequently coupled with a coarse-grained (CG) CFD-DEM. Within this multi-scale framework, the sand phase is modeled using a CG method, while biomass particles are tracked via DEM, and the fast pyrolysis of large biomass particles in a bubbling fluidized bed is comprehensively investigated. The results indicate that the predicted results of gas-solid dynamics and thermal behavior are satisfactory compared to experimental data, both at the single particle-scale and reactor-scale (cold and hot) validation. The product yields accounting for the intra-particle temperature gradient exhibit closer agreement with experimental data. Compared to neglecting the temperature gradients, the predicted tar yield has increased by approximately 5.71%. Furthermore, heat transfer at the particle-scale and the macroscopic gas-solid dynamics characteristics are accurately captured in pyrolysis. These evolution details are statistically and visually analyzed using probability density distribution and particle trajectory tracing, highlighting the accuracy and efficiency of the constructed multi-scale method. Future work will focus on predicting the pyrolysis behavior of large biomass particles with irregular shapes, thereby further broadening the applicability of this multi-scale method.

The growing severity of energy shortages and environmental challenges underscores the necessity of developing sustainable biomass energy. Rice husk (RH) biomass is abundantly available but often incinerated or discarded, causing environmental issues and resource underutilization. Converting RH into solid fuels in the form of formed coke offers a promising pathway for valorization. However, the inherently weak mechanical strength of formed coke hinders its transportation and applications. This study proposes a comprehensive strategy to produce high-strength formed coke, involving hydrothermal treatment (HT) of RH followed by sequential hot briquetting and carbonization. The effects of HT temperature (160–300 °C) on the structure, yield, and strength of the formed coke were systematically investigated. As a green pretreatment method, HT effectively disrupts RH structure, improves its heating value, and enhances the compactness and strength of the final product. The hydrothermally treated RH at 250 °C exhibited a 27% increase in heating value compared to raw RH, and achieved a high coke yield of 69% from the briquette. At the HT temperature of 230 °C, the formed coke achieved a maximum tensile strength of 32 MPa and a density of 1.4 g/cm3, which are 2.8 and 1.2 times higher than those of coke produced from non-treated RH, respectively. In addition, the strength mechanisms of the formed coke and briquette are discussed based on the characterization results of the products under various treatment conditions. This study provides valuable insights for the valorization of agricultural and forestry waste as high-quality fuels.

The rapid expansion of the new energy battery sector has significantly increased demand for key chemical precursors, including malonic acid (MA). To address this need, the development of efficient and sustainable production pathways is crucial. This study presents a comprehensive evaluation framework, integrating conceptual process design, techno-economic analysis (TEA), and life cycle assessment (LCA), to systematically assess the viability of two novel MA production routes. The first design employs a reactive distillation (RD) process catalyzed by cation exchange resin solid acids, while the second utilizes a microbial fermentation process (FP) with Pichia kudriavzevii converting glucose. TEA results demonstrate a clear economic advantage for the fermentation process, yielding a minimum selling price (MSP) of $1,892 per ton of MA. This contrasts with an MSP of $2,061 per ton for the reactive distillation process (SRD), representing a potential reduction of up to 69.7% compared to prevailing market prices. A sensitivity analysis, conducted via a hybrid machine learning method integrated with SHAP feature importance and 3D visualization, identifies raw material and major equipment costs as the most critical factors influencing economic feasibility. From an environmental perspective, the LCA reveals that the FP route generates substantially lower greenhouse gas (GHG) emissions (1.73 kg CO2-eq per kg MA) compared to the SRD process (2.73 kg CO2-eq per kg MA), constituting a 58% reduction. This marked contrast underscores the dual advantages of the fermentation pathway: it exhibits not only superior economic performance but also enhanced carbon efficiency. These findings position the FP route as a promising candidate for sustainable MA production, aligning with long-term goals for green chemical manufacturing in the energy storage supply chain.

Biomass fluidized bed gasifier (BFBG) is core equipment for producing sustainable syngas, which is one of the alternatives to replace fossil fuels in producing green fuels or chemicals. Multiscale simulations provide powerful tools to reveal the biomass gasification behaviors in fluidized bed reactors, thereby supporting the design, optimization, and scaling-up BFBG. This paper systematically reviews sub-models (molecular-scale, particle-scale, and meso-scale models) and reactor-scale models for the multiscale simulation of the BFBG. First, the advantages and disadvantages of various drying models, pyrolysis kinetics, and char gasification models at the molecular scale are discussed. The particle-scale models (i.e., zero-dimensional, corrected zero-dimensional, interface-based, and two-/three-dimensional models) are analyzed in terms of their accuracy and computational demands. The development of meso-scale models is also addressed. Additionally, machine learning methods used to assist in developing sub-models across different scales are summarized. Then, from the perspective of multiscale coupling between models at various scales, the strengths and limitations of reactor-scale models for multiscale simulation of BFBG are analyzed. Finally, the conclusions of this review and perspectives for future research, which aim to develop fast, reliable, and efficient models for the multiscale simulation of BFBG, are presented.

The prediction of correct drag and lift forces acting on immersed bodies is vital for optimized efficiency and industrial economics. Particulate suspensions involving non-spherical particles are extensively used in the chemical and medical industry. In recent years, the hydrodynamics of non-spherical particles have attracted much attention. The hydrodynamic forces acting on these particles are the function of particle size, shape, orientation, and the physical properties of fluid. In the current research, the drag and lift coefficient of non-spherical particles of disc and prolate shapes at different inclination angles and Reynolds number (Re) is calculated using the simplified Eulerian approach of computational fluid dynamics (CFD). The method adopted for simulation is first validated for a simple case of sphere. The effect of orientation and Re on drag and lift coefficient (C D and C L , respectively) is studied and compared with the available data. It is found that C D and C L increase as the angle of inclination increases, but decrease with Re. C D and C L are smaller for prolate at given Re and orientation than for the case of disc. The effects of finite size geometry cause some deviation in results from the literature. The results obtained with this simple and less computationally intensive method agree well with highly resolved numerical simulations with minor deviations.

Due to their superior mixing and heat transfer capabilities, fluidized beds are extensively utilized in chemical engineering, power generation, etc. Numerical simulations have long been essential for elucidating the nonlinear multiphase transfer processes within reactors. However, as the research perspective expands from lab-to pilot- and industrial-scale, the exponential increase in particle numbers constrains the applicability of multiphase flow models such as discrete element method (DEM), direct numerical simulation, etc. As an extension of traditional DEM methods, the coarse-grained (CG) DEM strategy effectively balances computational efficiency and accuracy. In order to promote the advancement of CG DEM in the field of fluidized beds, its development and applications are comprehensively reviewed in this work. First, the foundational principles of the CG method—similarity and energy conservation—are outlined. The scaling paradigms of the collision parameters, force formulations, and gas-solid properties are systematically listed in chronological order. Subsequently, the applications of the CG method across lab-, pilot-, and industrial-scale fluidized beds under both cold and heated conditions are summarized. Finally, future challenges and opportunities are highlighted. This review aims to accelerate the adoption of CG techniques in industrial-scale reactors while providing theoretical insights for optimizing existing models and developing novel scaling laws.