WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article CFD-DEM Investigation on Particle Separation from Fluid Flow Using Magnetic Fields(Elsevier, 2026) Morsali, Shaghayegh; Kazemi, Saman; Farahani, Farhang Jalali; Zarghami, RezaThis study presents a numerical simulation of magnetic particle separation from fluid flow using CFD-DEM modeling. Studies have shown that magnetic fields are an effective tool for particle separation, especially on small scales, and variables such as magnetic field intensity, fluid velocity, and particle size significantly impact separation efficiency. Other factors, such as the initial location of particles and their density, were also examined, and their effect on the attraction of particles was determined. The magnetic field was applied through a line dipole in the fluid channel. The simulation results show that particles accumulate in the channel area where the line dipole is located, with higher particle concentration at the beginning of the dipole compared to other sections. Additionally, the results indicate that increasing the magnetic field intensity significantly improves separation efficiency, while increasing fluid velocity can decrease this efficiency. At a velocity of 0.2 m per second, results showed that increasing the magnetic field intensity from 0.6 to 3 T improved the capture efficiency from 69 % to 91 %. Similarly, at a magnetic field intensity of 1 T, reducing the fluid velocity from 0.3 to 0.1 m per second doubled the capture efficiency. In the optimal state, combining maximum field intensity with minimum velocity can achieve an efficiency of 98 %. It was also observed that larger particle diameters and higher densities have a positive effect on particle attraction.Article Citation - WoS: 3Citation - Scopus: 3CFD-DEM Modeling of Biomass Pyrolysis in a DBD Plasma Fluidized Bed(Pergamon-Elsevier Science Ltd, 2025) Eslami, Ali; Kazemi, Saman; Hamidani, Golnaz; Zarghami, Reza; Mostoufi, NavidThis study developed a CFD-DEM model to simulate biomass pyrolysis within a dielectric barrier discharge (DBD) plasma fluidized bed reactor. Biomass, as a renewable energy source, offers a promising alternative for hydrogen production through pyrolysis. The integration of non-thermal plasma technology and fluidized bed reactors is expected to enhance conversion. Key operational parameters such as inlet gas velocity, particle size, and input voltage were examined to evaluate their effects on temperature distribution, particle conversion, and hydrogen production. Results indicated that higher inlet gas velocities promote better particle mixing and more uniform temperature and conversion distribution. Smaller particle sizes significantly enhance biomass conversion by increasing the available surface area between fluid and particles. Specifically, particles with diameters of 0.85, 1.2, and 1.5 mm achieved conversions of 10.4, 8.99, and 8.57 %, respectively, at 20 s from the start of the process. Additionally, increasing the input voltage increases the mean temperatures of particles and fluid, which enhances reaction rates and conversion. Optimizing these parameters can improve the efficiency of DBD plasmaassisted biomass pyrolysis, providing valuable insights for sustainable hydrogen production.Article A Novel ORC-PEM Integrated System for Sustainable Hydrogen Production from Low-Grade Waste Heat in Oil Refineries(Elsevier, 2025) Nazerifard, Reza; Mohammadpourfard, Mousa; Zarghami, RezaThis study presents an integrated multi-generation system for sustainable hydrogen production by harnessing low-grade waste heat from the overhead stream of the NHT unit's stripper column in an oil refinery. The proposed system integrates an ORC with a PEM electrolyzer, forming a novel energy solution that efficiently converts waste heat into clean hydrogen through electricity generation. A detailed model of the proposed system is developed, enabling a comprehensive assessment of its performance from thermodynamic, economic, and environmental viewpoints. At the same time, key operational parameters are optimized using the RSM-BBD method to minimize the hydrogen production cost, thereby enhancing the system's economic viability and practical implementation. The results demonstrated that the system achieves a yearly hydrogen production of 304.53 tons under optimized conditions, for 2.36 $/kg. The integrated system's overall energy and exergy efficiencies are calculated at 8.62 % and 33.43 %, respectively, demonstrating its high thermodynamic performance. Additionally, the system mitigates 3047 tons of CO2 annually by displacing conventional hydrogen production methods.Article Reversibility and Entropy in Bubbling Fluidized Beds: A Recurrence-Based Analysis(Elsevier, 2026) Zarghami, Reza; Mohammadpourfard, Mousa; Akkurt, Gulden GokcenNonlinear time series analysis techniques were applied to characterize bubbling fluidization. The delay method was used to reconstruct the state space attractor and analyze the reconstructed state space. The experiments were carried out in a laboratory-scale fluidized bed, operated under ambient conditions and with various sizes of particles, settled bed heights, measurement heights, and superficial gas velocities. The reversibility of the gas-solid fluidized bed hydrodynamics was investigated using pressure fluctuations by recurrence plot analysis. The anti-diagonal lines of the recurrence plot (RP) were regarded as a measure of reversibility. It was shown that the reversibility versus gas velocity has a concave shape in the bubbling regime. The highest reversibility occurs at velocities remarkably lower than the turbulent transition velocity. In addition, reversibility increases as the size of the particles increases. The Kolmogorov entropy was also estimated to confirm the reversibility analysis in the state space domain. In addition, the average cycle frequency and wideband energy in the frequency domain were also used to clarify the results in the state domain. It was found that a minimum in average cycle frequency, wideband energy, and entropy with an increase in the velocity corresponds to the transition between macro-structures and finer structures of the fluidization system. This minimum was primarily found in the macro-structures of the bubbling fluidization system. These findings can provide a practical tool for the optimal design and operation of the fluidized bed.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: 4Citation - Scopus: 2CFD-DEM Investigation of the Effects of Particle Size and Fluidization Regime on Heat Transfer in Fluidized Beds(Springer int Publ Ag, 2025) Alipoor, Mahdi; Kazemi, Saman; Zarghami, Reza; Mostoufi, NavidThis paper presents an in-depth study of heat transfer in fluidized beds, employing the CFD-DEM technique. The primary focus is to examine the impacts of inlet gas velocity, fluidization regime, and particle size on the thermal behavior of fluidized beds. The results revealed that thermal convection predominantly governs heat transfer in fluidized beds, accounting for the largest fraction of the overall heat transfer process. Particle-fluid-particle thermal conduction was found to contribute approximately 10-20% of the heat transfer, whereas particle-particle conduction exhibits a minor role. Upon increasing the inlet gas velocity, the convection rate intensifies, whereas the particle-fluid-particle conduction rate decreases. Furthermore, the study highlights the differences in temperature distribution between turbulent and bubbling fluidized beds. Turbulent bed demonstrated a more uniform and homogenous particle temperature compared to bubbling. At similar fluidization numbers in bubbling beds, increasing particle diameter enhances thermal convection while reducing particle-fluid-particle conduction. In contrast, the turbulent regime shows minimal differences in heat transfer mechanisms when particle size varies. Additionally, smaller particles are found to significantly improve temperature uniformity in fluidized beds. A comprehensive comparison of simulation results with experimental data validates the accuracy of the employed model, reinforcing its ability to predict heat transfer in fluidized beds reliably. This research provides valuable insights into the complex interplay of various mechanisms of heat transfer within fluidized beds, enabling engineers and researchers to optimize bed performance and enhance temperature control in various industrial applications.Article Citation - WoS: 3Citation - Scopus: 3On Digital Twins in Bioprocessing: Opportunities and Limitations(Elsevier Ltd, 2025) Shariatifar, Mehrdad; Rizi, Mohammadsadegh Salimian; Sotudeh-Gharebagh, Rahmat; Zarghami, Reza; Mostoufi, NavidIntegrating Digital Twins (DTs) in bioprocessing has become a prominent focus within the industry. Despite the challenges associated with implementing this technology in the field, the bioprocessing sector is interested in utilizing it. This is due to its potential to enhance process efficiency and overall profitability. The adoption of DTs is driven by the prospect of online monitoring, control, and optimization, enabling the products with precise and desired characteristics. To realize this objective, researchers propose a novel strategy for implementing DTs in bioprocessing. This involves the development of a hybrid model that combines first principal models and Machine Learning (ML) algorithms. This approach effectively addresses the limitations of previous methods and establishes a closed control loop system, continuously monitoring the system and adjusting input variables to achieve optimal outcomes. This study comprehensively explores various aspects of DTs. Firstly, it discusses the concept and characteristics of DTs, along with an examination of the advantages and challenges associated with their implementation. Secondly, it comprehensively analyzes key factors that directly influence DT implementation, including sensors, data collection, and models. Thirdly, it reviews the implications of Digital Solutions (DS) and DT in downstream and upstream bioprocessing. By providing theories, case studies, and practical frameworks, this work seeks to motivate both researchers and industry practitioners to adopt DT methodologies, thereby facilitating the emergence of enhanced precision, operational efficiency, and economic viability within biomanufacturing.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.