WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Citation - WoS: 3Citation - Scopus: 1Enhancing Biomass Pyrolysis via Microwave Heating: A CFD-DEM Study on Intensification in Fluidized Beds(Elsevier Sci Ltd, 2026) Hamidani, Golnaz; Kazemi, Saman; Eslami, Ali; Zarghami, Reza; Sotudeh-Gharebagh, Rahmat; Mostoufi, NavidBiomass conversion into high-value products in fluidized beds can be significantly improved by utilizing microwave irradiation as the heating source. The present work studied microwave-assisted biomass pyrolysis using a coupled CFD-DEM model in a fluidized bed. The effect of key operating parameters, including inlet gas velocity (1.5, 2, and 2.5 times the minimum fluidization velocity), mean particle diameter (1.2, 1.3, and 1.5 mm), and microwave power input (200, 400, and 600 W), was evaluated on the performance of the reactor. The results revealed that higher microwave power increased the mean particle temperature and chemical conversion rate due to greater internal energy generation within the biomass particles. Increasing the gas velocity led to lower particle temperature because of enhanced convective heat transfer to the gas phase, and improved the uniformity of temperature and conversion distributions. Furthermore, decreasing the mean particle diameter from 1.5 to 1.2 mm increased the average temperature, from 890 to 987 K, and raised biomass conversion from 14.8 to 18.1 %, mainly by reducing convective heat losses. The validated model developed in this study enables accurate predictions of process behavior and provides valuable insights for optimizing microwave-assisted biomass pyrolysis in fluidized beds. These findings highlight the potential of microwave-assisted fluidized bed pyrolysis as an efficient technique for process intensification in producing valuable bio-based products.Article Vibration-Assisted Fluidization of Nanocellulose(Elsevier, 2026) Salimi, Sina; Hoorijani, Hamed; Zarghami, Reza; Sotudeh-Gharebagh, Rahmat; Van Geem, Kevin M.Nanocellulose, a renewable nanomaterial prized for its mechanical strength, biocompatibility, and tunable properties, faces challenges in gas-solid fluidization due to nanoparticle agglomeration, weak gas-solid interactions, and high elutriation caused by strong interparticle forces. This study uses pressure fluctuation analysis across frequency and time-frequency (wavelet transform) domains to investigate nanocellulose fluidization in a gas-solid bed. Mechanical vibration was introduced to optimize fluidization, with effects compared against nonvibrated conditions. Results show vibration significantly reduces agglomerate size and enhances bed expansion, improving fluidization efficiency. Notably, vibration lowers the minimum gas velocity requirement by approximately 4-fold. Pressure fluctuation analysis reveals that vibration amplifies low-frequency energy, fostering smaller bubbles and shifting energy contributions from large agglomerates to finer hydrodynamic structures. This shift correlates with intensified agglomerate interactions, leading to breakup and size reduction. Finally, the effect of introducing a powder additive to the nanocellulose bed on the hydrodynamics was examined, showing a moderate rise in macroscale energy at 1 % additive loading and a pronounced shift at 2 %, where macro structures accounted for nearly 45 % of the spectral energy. Overall, these findings underscore vibration-assisted fluidization as a promising method for scalable nanocellulose processing, offering actionable insights for advancing industrial applications.Article Citation - WoS: 1Citation - Scopus: 1A Novel Ml-Dem Algorithm for Predicting Particle Motion in Rotary Drums(Elsevier Sci Ltd, 2025) Kazemi, Saman; Zarghami, Reza; Mostoufi, Navid; Sotudeh-Gharebagh, Rahmat; Al-Raoush, Riyadh I.The discrete element method (DEM) is a widely used approach for studying the behavior of particles in industrial equipment, including rotary drums. Although DEM is highly accurate and efficient, it suffers from the computational cost in simulations. The primary objective of this research is to reduce the computational costs of DEM by introducing a novel machine learning (ML) approach based on a deep neural network for predicting particle behavior in rotary drums. The proposed approach utilizes a continuous convolution operator in a neural network. To evaluate its effectiveness, the results of the proposed ML-DEM approach were compared quantitatively and qualitatively with the experimental data and the conventional DEM results. It was shown that in addition to its high accuracy, the proposed approach reduces the computational costs by approximately 35 % and 65 % compared to the conventional DEM simulations on GPU and CPU (with 8 processors), respectively. Furthermore, to ensure the comprehensive and independent validation of the proposed algorithm, the study investigated the effects of various parameters such as drum rotational speed and fill ratio on lateral entropy-based mixing, circulation time, and velocity profile in the active layer. The results were then compared with those obtained using the conventional DEM and found to be in good agreement. This new algorithm can serve as a starting point for reducing computational costs in simulating particle motion in granular systems.Article Citation - WoS: 4Citation - Scopus: 3Exploration of Electrostatics Effect on Dispersion and Coating Mechanisms in Dry Powder Inhalers by Discrete Element Method(Elsevier, 2025) Saeid, Pooya; Kazemi, Saman; Zarghami, Reza; Sotudeh-Gharebagh, Rahmat; Mostoufi, NavidImproving drug delivery in the respiratory system relies on the effective coating and dispersion of active pharmaceutical ingredients (APIs) in dry powder inhalers (DPIs) and the respiratory system's airways. This study aims to explore the impact of different factors on coating APIs on carrier particles, considering electrostatic and van der Waals forces using the discrete element method (DEM). This study focuses on the critical elements of API dispersion, specifically collisions between API-coated carrier particles with each other and DPI walls. The factors influencing the dispersion ratio in these collisions, such as impact velocity, contact angle, and particle charge, are examined. Additionally, a reduced-scale shaking DPI with three frequencies is used to investigate the API coating mechanism on carriers, which was not explored in previous studies. The difference in work function between carrier particles and APIs generates charge in the shaking DPI due to collisions. This causes electrostatic force to dominate over van der Waals force, breaking agglomerates and attaching APIs to carrier particles. This study shows that the amount of generated charge increases with particle collisions and that charge distribution becomes more balanced over time through charge exchange between particles. By elucidating the relationships among impact velocity, dispersion ratio, shaking frequency, and contact angles, this study paves the way for future research on more efficient DPI designs.