IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v254y2022ipbs0360544222012233.html
   My bibliography  Save this article

Machine learning prediction of the yield and oxygen content of bio-oil via biomass characteristics and pyrolysis conditions

Author

Listed:
  • Yang, Ke
  • Wu, Kai
  • Zhang, Huiyan

Abstract

The bio-oil produced from biomass pyrolysis offers an important potential alternative to fossil fuels, but the yield and composition of pyrolysis product are impacted by many conditions. This work aims to predict the yield and oxygen content of bio-oil via machine learning tools based on biomass characteristics and pyrolysis conditions. For this purpose, the Random Forest (RF) algorithm is introduced and successfully applied. The performances of trained prediction models are assessed based on the regression coefficient (R2) for the test data. The results shows that the Proximate-Yield model (R2 = 0.925) has the best performance for predicting bio-oil yield, and the Ultimate-O model (R2 = 0.895) has the best performance for predicting the oxygen content of bio-oil. According to feature importance analysis, the heating rate occupied the biggest importance for predicting bio-oil yield, and the internal information of biomass is more important than that of pyrolysis conditions for predicting the bio-oil oxygen content. Besides, the modes of each variable affecting the bio-oil yield and oxygen content are described by partial dependence analysis. This work will provide a new insight for controlling the yield and oxygen content of bio-oil, which is helpful to facilitate the process optimization in engineering application.

Suggested Citation

  • Yang, Ke & Wu, Kai & Zhang, Huiyan, 2022. "Machine learning prediction of the yield and oxygen content of bio-oil via biomass characteristics and pyrolysis conditions," Energy, Elsevier, vol. 254(PB).
  • Handle: RePEc:eee:energy:v:254:y:2022:i:pb:s0360544222012233
    DOI: 10.1016/j.energy.2022.124320
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222012233
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2022.124320?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nam, Hyungseok & Capareda, Sergio C. & Ashwath, Nanjappa & Kongkasawan, Jinjuta, 2015. "Experimental investigation of pyrolysis of rice straw using bench-scale auger, batch and fluidized bed reactors," Energy, Elsevier, vol. 93(P2), pages 2384-2394.
    2. Xing, Jiangkuan & Luo, Kun & Wang, Haiou & Gao, Zhengwei & Fan, Jianren, 2019. "A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches," Energy, Elsevier, vol. 188(C).
    3. Brand, Steffen & Hardi, Flabianus & Kim, Jaehoon & Suh, Dong Jin, 2014. "Effect of heating rate on biomass liquefaction: Differences between subcritical water and supercritical ethanol," Energy, Elsevier, vol. 68(C), pages 420-427.
    4. Septien, S. & Escudero Sanz, F.J. & Salvador, S. & Valin, S., 2018. "The effect of pyrolysis heating rate on the steam gasification reactivity of char from woodchips," Energy, Elsevier, vol. 142(C), pages 68-78.
    5. Leng, Lijian & Li, Hui & Yuan, Xingzhong & Zhou, Wenguang & Huang, Huajun, 2018. "Bio-oil upgrading by emulsification/microemulsification: A review," Energy, Elsevier, vol. 161(C), pages 214-232.
    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. Wu, Kai & Yang, Ke & Zhu, Yiwen & Luo, Bingbing & Chu, Chenyang & Li, Mingfan & Zhang, Yuanjian & Zhang, Huiyan, 2023. "The co-pyrolysis interactionsof isolated lignins and cellulose by experiments and theoretical calculations," Energy, Elsevier, vol. 263(PC).
    2. Wang, Zhengxin & Peng, Xinggan & Xia, Ao & Shah, Akeel A. & Yan, Huchao & Huang, Yun & Zhu, Xianqing & Zhu, Xun & Liao, Qiang, 2023. "Comparison of machine learning methods for predicting the methane production from anaerobic digestion of lignocellulosic biomass," Energy, Elsevier, vol. 263(PD).
    3. Md Sumon Reza & Zhanar Baktybaevna Iskakova & Shammya Afroze & Kairat Kuterbekov & Asset Kabyshev & Kenzhebatyr Zh. Bekmyrza & Marzhan M. Kubenova & Muhammad Saifullah Abu Bakar & Abul K. Azad & Hrido, 2023. "Influence of Catalyst on the Yield and Quality of Bio-Oil for the Catalytic Pyrolysis of Biomass: A Comprehensive Review," Energies, MDPI, vol. 16(14), pages 1-39, July.

    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. Lee, Seokhwan & Woo, Sang Hee & Kim, Yongrae & Choi, Young & Kang, Kernyong, 2020. "Combustion and emission characteristics of a diesel-powered generator running with N-butanol/coffee ground pyrolysis oil/diesel blended fuel," Energy, Elsevier, vol. 206(C).
    2. Kim, Jun Young & Kim, Dongjae & Li, Zezhong John & Dariva, Claudio & Cao, Yankai & Ellis, Naoko, 2023. "Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach," Energy, Elsevier, vol. 263(PC).
    3. Kumar, R. & Strezov, V., 2021. "Thermochemical production of bio-oil: A review of downstream processing technologies for bio-oil upgrading, production of hydrogen and high value-added products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    4. Wang, Xun & Fu, Genshen & Xiao, Bo & Xu, Tingting, 2022. "Optimization of nickel-iron bimetallic oxides for coproduction of hydrogen and syngas in chemical looping reforming with water splitting process," Energy, Elsevier, vol. 246(C).
    5. Xing Yang & Hailong Wang & Peter James Strong & Song Xu & Shujuan Liu & Kouping Lu & Kuichuan Sheng & Jia Guo & Lei Che & Lizhi He & Yong Sik Ok & Guodong Yuan & Ying Shen & Xin Chen, 2017. "Thermal Properties of Biochars Derived from Waste Biomass Generated by Agricultural and Forestry Sectors," Energies, MDPI, vol. 10(4), pages 1-12, April.
    6. Wang, Liping & Chang, Yuzhi & Li, Aimin, 2019. "Hydrothermal carbonization for energy-efficient processing of sewage sludge: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 423-440.
    7. M. N. Uddin & Kuaanan Techato & Juntakan Taweekun & Md Mofijur Rahman & M. G. Rasul & T. M. I. Mahlia & S. M. Ashrafur, 2018. "An Overview of Recent Developments in Biomass Pyrolysis Technologies," Energies, MDPI, vol. 11(11), pages 1-24, November.
    8. Rodriguez-Alejandro, David A. & Nam, Hyungseok & Maglinao, Amado L. & Capareda, Sergio C. & Aguilera-Alvarado, Alberto F., 2016. "Development of a modified equilibrium model for biomass pilot-scale fluidized bed gasifier performance predictions," Energy, Elsevier, vol. 115(P1), pages 1092-1108.
    9. Chen, Xiaoling & Zhang, Yongxing & Xu, Baoshen & Li, Yifan, 2022. "A simple model for estimation of higher heating value of oily sludge," Energy, Elsevier, vol. 239(PA).
    10. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    11. Muhammad Usman & Shuo Cheng & Sasipa Boonyubol & Jeffrey S. Cross, 2023. "Evaluating Green Solvents for Bio-Oil Extraction: Advancements, Challenges, and Future Perspectives," Energies, MDPI, vol. 16(15), pages 1-45, August.
    12. Brand, Steffen & Kim, Jaehoon, 2015. "Liquefaction of major lignocellulosic biomass constituents in supercritical ethanol," Energy, Elsevier, vol. 80(C), pages 64-74.
    13. Yang, Shiliang & Dong, Ruihan & Du, Yanxiang & Wang, Shuai & Wang, Hua, 2021. "Numerical study of the biomass pyrolysis process in a spouted bed reactor through computational fluid dynamics," Energy, Elsevier, vol. 214(C).
    14. Ong, Benjamin H.Y. & Walmsley, Timothy G. & Atkins, Martin J. & Varbanov, Petar S. & Walmsley, Michael R.W., 2019. "A heat- and mass-integrated design of hydrothermal liquefaction process co-located with a Kraft pulp mill," Energy, Elsevier, vol. 189(C).
    15. Campuzano, Felipe & Brown, Robert C. & Martínez, Juan Daniel, 2019. "Auger reactors for pyrolysis of biomass and wastes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 372-409.
    16. Guzelciftci, Begum & Park, Ki-Bum & Kim, Joo-Sik, 2020. "Production of phenol-rich bio-oil via a two-stage pyrolysis of wood," Energy, Elsevier, vol. 200(C).
    17. Anna Matveeva & Aleksey Bychkov, 2022. "How to Train an Artificial Neural Network to Predict Higher Heating Values of Biofuel," Energies, MDPI, vol. 15(19), pages 1-13, September.
    18. Knez, Ž. & Markočič, E. & Leitgeb, M. & Primožič, M. & Knez Hrnčič, M. & Škerget, M., 2014. "Industrial applications of supercritical fluids: A review," Energy, Elsevier, vol. 77(C), pages 235-243.
    19. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
    20. Marwa G. Saad & Noura S. Dosoky & Mohamed S. Zoromba & Hesham M. Shafik, 2019. "Algal Biofuels: Current Status and Key Challenges," Energies, MDPI, vol. 12(10), pages 1-22, May.

    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:eee:energy:v:254:y:2022:i:pb:s0360544222012233. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.