Browsing by Author "Ramesh, P."

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    Advances in Computational Fluid Dynamics Modeling for Biomass Pyrolysis: A Review
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Kulkarni, A.; Mishra, G.; Palla, S.; Ramesh, P.; Surya, D.V.; Basak, T.
    Pyrolysis, a process for extracting valuable chemicals from waste materials, leverages computational fluid dynamics (CFD) to optimize reactor parameters, thereby enhancing product quality and process efficiency. This review aims to understand the application of CFD in pyrolysis. Initially, the need for pyrolysis and its role in biomass valorization are discussed, and this is followed by an elaboration of the fundamentals of CFD studies in terms of their application to the pyrolysis process. The various CFD simulations and models used to understand product formation are also explained. Pyrolysis is conducted using both conventional and microwave-assisted pyrolysis platforms. Hence, the reaction kinetics, governing model equations, and laws are discussed in the conventional pyrolysis section. In the microwave-assisted pyrolysis section, the importance of wavelength, penetration depth, and microwave conversion efficiencies on the CFD are discussed. This review provides valuable insights to academic researchers on the application of CFD in pyrolysis systems. The modeling of pyrolysis by computational fluid dynamics (CFD) is a complex process due to the implementation of multiple reaction kinetics and physics, high computational cost, and reactor design. These challenges in the modeling of the pyrolysis process are discussed in this paper. Significant solutions that have been used to overcome the challenges are also provided with potential areas of research and development in the future of CFD in pyrolysis. © 2023 by the authors.
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    Advances in Computational Fluid Dynamics Modeling for Biomass Pyrolysis: A Review
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Kulkarni, A.; Mishra, G.; Palla, S.; Ramesh, P.; Surya, D.V.; Basak, T.
    Pyrolysis, a process for extracting valuable chemicals from waste materials, leverages computational fluid dynamics (CFD) to optimize reactor parameters, thereby enhancing product quality and process efficiency. This review aims to understand the application of CFD in pyrolysis. Initially, the need for pyrolysis and its role in biomass valorization are discussed, and this is followed by an elaboration of the fundamentals of CFD studies in terms of their application to the pyrolysis process. The various CFD simulations and models used to understand product formation are also explained. Pyrolysis is conducted using both conventional and microwave-assisted pyrolysis platforms. Hence, the reaction kinetics, governing model equations, and laws are discussed in the conventional pyrolysis section. In the microwave-assisted pyrolysis section, the importance of wavelength, penetration depth, and microwave conversion efficiencies on the CFD are discussed. This review provides valuable insights to academic researchers on the application of CFD in pyrolysis systems. The modeling of pyrolysis by computational fluid dynamics (CFD) is a complex process due to the implementation of multiple reaction kinetics and physics, high computational cost, and reactor design. These challenges in the modeling of the pyrolysis process are discussed in this paper. Significant solutions that have been used to overcome the challenges are also provided with potential areas of research and development in the future of CFD in pyrolysis. © 2023 by the authors.
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    Performance assessment of waste heat/solar driven membrane-based simultaneous desalination and liquid desiccant regeneration system using a thermal model and KNN machine learning tool
    (Elsevier B.V., 2021) Kiran Naik, B.; Chinthala, M.; Patel, S.; Ramesh, P.
    In this work, the waste heat/solar heat-driven membrane-based liquid desiccant regenerator performance, as well as desalinated water extraction rate, are predicted and analyzed by developing a thermal model and KNN–ML tool. In the membrane-based liquid desiccant regenerator, water is used as a working fluid instead of scavenging air for desalinated water extraction purpose. The proposed thermal model and KNN-ML tool are validated with the literature data and found in good agreement. Optimal inlet conditions were determined for the given operating range using the thermal model and KNN–ML tool. With vapor flux and energy exchange as the performance indicators, the water extraction rate and thermal performance of the membrane-based liquid desiccant regenerator are predicted for the optimal inlet condition using the KNN-ML tool. Also, the heat and mass transfer characteristics such as Lewis number and NTUm across the membrane in the liquid desiccant regenerator are assessed using the developed thermal model. Further, for the optimal inlet conditions, utilizing waste heat from thermal power plants (Method–I) and solar energy from solar heater (Method–II), the thermal performance and water extraction rate across the membrane in the liquid desiccant regenerator are assessed based on the developed thermal model. © 2021 Elsevier B.V.
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    Recent advancements of CFD and heat transfer studies in pyrolysis: A review
    (Elsevier B.V., 2023) Dadi, V.S.; Sridevi, V.; Tanneru, H.K.; Busigari, R.R.; Ramesh, P.; Kulkarni, A.; Mishra, G.; Basak, T.
    There is a pressing need to process the solid waste by using pyrolysis technology due to its uniqueness to produce various solid, liquid and gaseous products. However, further understanding of pyrolysis process is needed. Most importantly, the role of computational fluid dynamics (CFD) in pyrolysis is to be thoroughly investigated. In recent times, there has been significant progress in the research works aligned with evaluating the role of CFD in biomass pyrolysis. Hence, the current review manuscript focusses the current state of the art in the application of CFD tools to multi-scale biomass pyrolysis systems. Modeling of fluid and heat transport in conventional pyrolysis reactors, microwave-assisted pyrolysis reactors, and solar-assisted pyrolysis reactors for the conversion of biomass have been critically analyzed. The theoretical basis and the practical applicability of the CFD models to efficiently emulate and predict the overall complexity of pyrolysis process for the multi-scale and multi-phase nature of biomass have been discussed. However, the validity and accuracy of the CFD models needs to be enhanced. In the future directions, the steps for expanding the applicability of these theoretical and computational models have been outlined. This review would provide detailed understanding of CFD role in pyrolysis process conducted in various reactor systems. © 2023 Elsevier B.V.
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    Synergistic effects and product yields in microwave-assisted in-situ co-pyrolysis of rice straw and paraffin wax
    (Institution of Chemical Engineers, 2024) Hamzah, H.T.; Sridevi, V.; Surya, D.V.; Ramesh, P.; Sankar Rao, C.; Palla, S.; Abdullah, T.A.
    Microwave-assisted pyrolysis is one of the most efficient methods for solid waste management. This study employed microwave-assisted catalytic co-pyrolysis to convert Paraffin wax (PW) and rice straw (RS) into valuable char, gas, and oil products. KOH and graphite were used as the catalyst and susceptor, respectively. The RS and PW blend served as the feedstock (with a blend ratio of 0–10 g). The yields of co-pyrolysis at different blending ratios of RS: PW exhibited variations in char content (ranging from 9.8% to 22.6% by wt.), oil production (ranging from 34.1% to 76.9% by wt.), and gas formation (ranging from 13.2% to 47.5% by wt.). The effects of the RS: PW ratio on the average heating rate, feedstock conversion, and product yields were also investigated. Analyses were performed to assess the synergistic impacts on product yields, average heating rates, and conversion factors. Notably, co-pyrolysis synergy led to increased oil and char production. Furthermore, we conducted FTIR analysis on the oil and char produced through the catalytic co-pyrolysis of RS: PW. In conjunction with co-pyrolysis synergy, the catalyst facilitated the formation of amides, alkenes, aliphatic compounds, and aromatic compounds. © 2023 The Institution of Chemical Engineers
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    Two-step synthesis of biochar using torrefaction and microwave-assisted pyrolysis: Understanding the effects of torrefaction temperature and catalyst loading
    (Elsevier B.V., 2023) Ramesh, P.; Sankar Rao, C.S.; Surya, D.V.; Sridevi, V.; Kulkarni, A.
    The study focused on synthesizing the biochar from sawdust using torrefaction followed by pyrolysis. Sawdust was torrefied at different temperatures (125 °C, 150 °C, and 175 °C) using a conventional hot air oven. The obtained torrefied biochar was subjected to Microwave-assisted pyrolysis at a power of 300 W for 10 min. Graphite was used as a susceptor, and KOH was used as a catalyst. The maximum biochar product yield varied from 24 to 48 wt% and increased with torrefaction temperature. The average heating rates ranged from 54.5 to 74.6 °C/min. At 10 g of KOH, higher heating rates were obtained. The pyrolysis index analyzed varied between 97.5 and 111.5 and decreased with the increase in torrefaction temperature. The obtained biochar was analyzed using SEM, BET, XRD, FTIR, ICP-OES, and Raman spectroscopy. Porous structure formation enhanced, and the concentrations of Ca, Al, and Fe decreased with the increase in torrefaction temperature. © 2023 Elsevier B.V.
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    Utilizing support vector regression modeling to predict pyro product yields from microwave-assisted catalytic co-pyrolysis of biomass and waste plastics
    (Elsevier Ltd, 2023) Ramesh, P.; Sankar Rao, C.S.; Surya, D.V.; Kumar, A.; Basak, T.
    The rise in plastic waste production has led to the development of co-pyrolysis of waste plastics and biomass as a potential solution. This process converts waste into valuable resources, including chemicals and pollutant-absorbing materials. Accurately predicting product yields is crucial and involves considering feedstock characteristics and pyrolysis conditions. No previous work on machine learning (ML) predicts pyro-products considering catalyst and blend as input features. This study used a support vector machine (SVM) to predict pyro-product yields from microwave-assisted co-pyrolysis of biomass and plastics. SVM models were trained, validated, and then applied to new data. The results showed high predictive accuracy, with R2 values of 0.96, 0.93, and 0.91 for bio-oil, biochar, and biogas, respectively. The SVM model demonstrated strong predictive capabilities, indicating effective generalization ability based on statistical parameters. Additionally, SVM models incorporating all features performed better than those based on 'elementary analysis (EA)' and 'proximate analysis (PA)' alone. The pearson correlation coefficient (PCC) approach assessed the correlation between input features to remove highly correlated variables. The partial dependence analysis reveals the individual effects of influential factors and their interactions in the co-pyrolysis process, highlighting significant features like carbon, hydrogen, ash, volatile matter, and nitrogen content that influence oil, char, and gas yields, thereby providing valuable insights for optimization strategies in co-pyrolysis. © 2023 Elsevier Ltd