Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Author: search.filters.author.Sotudeh-Gharebagh, RahmatPublication Index: search.filters.publicationIndex.Scopus

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  • Article
    Citation - WoS: 3
    Citation - Scopus: 1
    Enhancing 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, Navid
    Biomass 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
    Citation - WoS: 3
    Citation - Scopus: 3
    On Digital Twins in Bioprocessing: Opportunities and Limitations
    (Elsevier Ltd, 2025) Shariatifar, Mehrdad; Rizi, Mohammadsadegh Salimian; Sotudeh-Gharebagh, Rahmat; Zarghami, Reza; Mostoufi, Navid
    Integrating 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: 1
    Citation - Scopus: 1
    A 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: 4
    Citation - Scopus: 3
    Exploration 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, Navid
    Improving 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.