IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p950-d736400.html
   My bibliography  Save this article

Thermal Analysis and Kinetic Modeling of Pyrolysis and Oxidation of Hydrochars

Author

Listed:
  • Gabriella Gonnella

    (Department of Civil, Environmental and Mechanical Engineering, University of Trento, 38123 Trento, Italy)

  • Giulia Ischia

    (Department of Civil, Environmental and Mechanical Engineering, University of Trento, 38123 Trento, Italy)

  • Luca Fambri

    (Department of Industrial Engineering, University of Trento, Via Sommarive 9, 38123 Trento, Italy)

  • Luca Fiori

    (Department of Civil, Environmental and Mechanical Engineering, University of Trento, 38123 Trento, Italy)

Abstract

This study examines the kinetics of pyrolysis and oxidation of hydrochars through thermal analysis. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) techniques were used to investigate the decomposition profiles and develop two distributed activation energy models (DAEM) of hydrochars derived from the hydrothermal carbonization of grape seeds produced at different temperatures (180, 220, and 250 °C). Data were collected at 1, 3, and 10 °C/min between 30 and 700 °C. TGA data highlighted a decomposition profile similar to that of the raw biomass for hydrochars obtained at 180 and 220 °C (with a clear distinction between oil, cellulosic, hemicellulosic, and lignin-like compounds), while presenting a more stable profile for the 250 °C hydrochar. DSC showed a certain exothermic behavior during pyrolysis of hydrochars, an aspect also investigated through thermodynamic simulations in Aspen Plus. Regarding the DAEM, according to a Gaussian model, the severity of the treatment slightly affects kinetic parameters, with average activation energies between 193 and 220 kJ/mol. Meanwhile, the Miura–Maki model highlights the distributions of the activation energy and the pre-exponential factor during the decomposition.

Suggested Citation

  • Gabriella Gonnella & Giulia Ischia & Luca Fambri & Luca Fiori, 2022. "Thermal Analysis and Kinetic Modeling of Pyrolysis and Oxidation of Hydrochars," Energies, MDPI, vol. 15(3), pages 1-21, January.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:950-:d:736400
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/950/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/3/950/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alexandre D’Lamare Maia de Medeiros & Cláudio José Galdino da Silva Junior & Julia Didier Pedrosa de Amorim & Helenise Almeida do Nascimento & Attilio Converti & Andréa Fernanda de Santana Costa & Leo, 2021. "Biocellulose for Treatment of Wastewaters Generated by Energy Consuming Industries: A Review," Energies, MDPI, vol. 14(16), pages 1-19, August.
    2. Roberta Ferrentino & Fabio Merzari & Luca Fiori & Gianni Andreottola, 2020. "Coupling Hydrothermal Carbonization with Anaerobic Digestion for Sewage Sludge Treatment: Influence of HTC Liquor and Hydrochar on Biomethane Production," Energies, MDPI, vol. 13(23), pages 1-19, November.
    3. Li, Jie & Pan, Lanjia & Suvarna, Manu & Tong, Yen Wah & Wang, Xiaonan, 2020. "Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning," Applied Energy, Elsevier, vol. 269(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Janaki Komandur & Abhishek Kumar & Preethi Para & Kaustubha Mohanty, 2022. "Kinetic Parameters Estimation of Thermal and Co-Pyrolysis of Groundnut De-oiled Cake and Polyethylene Terephthalate (PET) Waste," Energies, MDPI, vol. 15(20), pages 1-12, October.
    2. Xiangxi Wang & Zhenzhong Hu & Inamullah Mian & Omar D. Dacres & Jian Li & Bo Wei & Mei Zhong & Xian Li & Noor Rahman & Guangqian Luo & Hong Yao, 2022. "Gasification Kinetics of Organic Solid Waste Pellets: Comparative Study Using Distributed Activation Energy Model and Coats–Redfern Method," Energies, MDPI, vol. 15(24), pages 1-12, December.
    3. M. Toufiq Reza, 2022. "Hydrothermal Carbonization," Energies, MDPI, vol. 15(15), pages 1-3, July.
    4. Bartłomiej Igliński & Wojciech Kujawski & Urszula Kiełkowska, 2023. "Pyrolysis of Waste Biomass: Technical and Process Achievements, and Future Development—A Review," Energies, MDPI, vol. 16(4), pages 1-26, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giuseppe Campo & Alberto Cerutti & Claudio Lastella & Aldo Leo & Deborah Panepinto & Mariachiara Zanetti & Barbara Ruffino, 2021. "Production and Destination of Sewage Sludge in the Piemonte Region (Italy): The Results of a Survey for a Future Sustainable Management," IJERPH, MDPI, vol. 18(7), pages 1-13, March.
    2. Zhi Zou & Longcheng Liu & Shuo Meng & Xiaolei Bian & Yongmei Li, 2021. "Applicability of Different Double-Layer Models for the Performance Assessment of the Capacitive Energy Extraction Based on Double Layer Expansion (CDLE) Technique," Energies, MDPI, vol. 14(18), pages 1-22, September.
    3. Ihsan Hamawand, 2023. "Energy Consumption in Water/Wastewater Treatment Industry—Optimisation Potentials," Energies, MDPI, vol. 16(5), pages 1-3, March.
    4. Onsree, Thossaporn & Tippayawong, Nakorn & Phithakkitnukoon, Santi & Lauterbach, Jochen, 2022. "Interpretable machine-learning model with a collaborative game approach to predict yields and higher heating value of torrefied biomass," Energy, Elsevier, vol. 249(C).
    5. Mu, Lin & Wang, Zhen & Sun, Meng & Shang, Yan & Pu, Hang & Dong, Ming, 2024. "Machine learning model with a novel self–adjustment method: A powerful tool for predicting biomass ash fusibility and enhancing its potential applications," Renewable Energy, Elsevier, vol. 237(PA).
    6. Krystian Krochmalny & Halina Pawlak-Kruczek & Norbert Skoczylas & Mateusz Kudasik & Aleksandra Gajda & Renata Gnatowska & Monika Serafin-Tkaczuk & Tomasz Czapka & Amit K. Jaiswal & Vishwajeet & Amit A, 2022. "Use of Hydrothermal Carbonization and Cold Atmospheric Plasma for Surface Modification of Brewer’s Spent Grain and Activated Carbon," Energies, MDPI, vol. 15(12), pages 1-11, June.
    7. Shahbeik, Hossein & Rafiee, Shahin & Shafizadeh, Alireza & Jeddi, Dorsa & Jafary, Tahereh & Lam, Su Shiung & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Characterizing sludge pyrolysis by machine learning: Towards sustainable bioenergy production from wastes," Renewable Energy, Elsevier, vol. 199(C), pages 1078-1092.
    8. Djandja, Oraléou Sangué & Duan, Pei-Gao & Yin, Lin-Xin & Wang, Zhi-Cong & Duo, Jia, 2021. "A novel machine learning-based approach for prediction of nitrogen content in hydrochar from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 232(C).
    9. Beata Piotrowska & Daniel Słyś, 2022. "Comprehensive Analysis of the State of Technology in the Field of Waste Heat Recovery from Grey Water," Energies, MDPI, vol. 16(1), pages 1-20, December.
    10. Zhang, Bowei & Guo, Simao & Jin, Hui, 2022. "Production forecast analysis of BP neural network based on Yimin lignite supercritical water gasification experiment results," Energy, Elsevier, vol. 246(C).
    11. Büyükkanber, Kaan & Haykiri-Acma, Hanzade & Yaman, Serdar, 2023. "Calorific value prediction of coal and its optimization by machine learning based on limited samples in a wide range," Energy, Elsevier, vol. 277(C).
    12. Gregor Sailer & Julian Comi & Florian Empl & Martin Silberhorn & Valeska Heymann & Monika Bosilj & Siham Ouardi & Stefan Pelz & Joachim Müller, 2022. "Hydrothermal Treatment of Residual Forest Wood (Softwood) and Digestate from Anaerobic Digestion—Influence of Temperature and Holding Time on the Characteristics of the Solid and Liquid Products," Energies, MDPI, vol. 15(10), pages 1-26, May.
    13. Gabriel Gerner & Luca Meyer & Rahel Wanner & Thomas Keller & Rolf Krebs, 2021. "Sewage Sludge Treatment by Hydrothermal Carbonization: Feasibility Study for Sustainable Nutrient Recovery and Fuel Production," Energies, MDPI, vol. 14(9), pages 1-12, May.
    14. Parthasarathy Velusamy & Jagadeesan Srinivasan & Nithyaselvakumari Subramanian & Rakesh Kumar Mahendran & Muhammad Qaiser Saleem & Maqbool Ahmad & Muhammad Shafiq & Jin-Ghoo Choi, 2023. "Optimization-Driven Machine Learning Approach for the Prediction of Hydrochar Properties from Municipal Solid Waste," Sustainability, MDPI, vol. 15(7), pages 1-14, March.
    15. Tan, Daniel & Suvarna, Manu & Shee Tan, Yee & Li, Jie & Wang, Xiaonan, 2021. "A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing," Applied Energy, Elsevier, vol. 291(C).
    16. Djandja, Oraléou Sangué & Salami, Adekunlé Akim & Wang, Zhi-Cong & Duo, Jia & Yin, Lin-Xin & Duan, Pei-Gao, 2022. "Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 245(C).
    17. Yang, Yadong & Shahbeik, Hossein & Shafizadeh, Alireza & Masoudnia, Nima & Rafiee, Shahin & Zhang, Yijia & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries," Renewable Energy, Elsevier, vol. 201(P2), pages 70-86.
    18. Joshua O. Ighalo & Florence C. Akaeme & Jordana Georgin & Jivago Schumacher de Oliveira & Dison S. P. Franco, 2025. "Biomass Hydrochar: A Critical Review of Process Chemistry, Synthesis Methodology, and Applications," Sustainability, MDPI, vol. 17(4), pages 1-44, February.
    19. Li, Jie & Suvarna, Manu & Pan, Lanjia & Zhao, Yingru & Wang, Xiaonan, 2021. "A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification," Applied Energy, Elsevier, vol. 304(C).
    20. Zheng, Xidong & Zhou, Sheng & Jin, Tao, 2023. "A new machine learning-based approach for cross-region coupled wind-storage integrated systems identification considering electricity demand response and data integration: A new provincial perspective," Energy, Elsevier, vol. 283(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:950-:d:736400. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.