IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v225y2024ics0960148124002830.html
   My bibliography  Save this article

Prediction of bio-oil yield by machine learning model based on 'enhanced data' training

Author

Listed:
  • Zhao, Chenxi
  • Lu, Xueying
  • Jiang, Zihao
  • Ma, Huan
  • Chen, Juhui
  • Liu, Xiaogang

Abstract

Bio-oil is widely used and has great application potential. With the development of artificial intelligence, machine learning has been gradually applied in the field of biomass, the data augmentation method is a common operation method for training models in the field of computer or data processing. In this study, the concept of 'enhanced data' was proposed for the first time. According to the composition characteristics of biomass raw materials, the sample data were divided into three different sample sets. Based on the light gradient boosting machine (Light GBM) and deep neural network (DNN) algorithm, a prediction model of bio-oil yield was established. Combined with the analysis of partial correlation, the influence of 'enhanced data' on the prediction accuracy of the model was explored. The results showed that the prediction accuracy of the model was improved to a certain extent after adding 'enhanced data'. The Light GBM model was more suitable for predicting bio-oil yield, and the Light GBM _ c model performed best, with R2 of 0.894, MAE of 3.622, and RMSE of 4.445. At the same time, this study also has certain reference significance for other prediction studies in the field of biomass thermochemical conversion.

Suggested Citation

  • Zhao, Chenxi & Lu, Xueying & Jiang, Zihao & Ma, Huan & Chen, Juhui & Liu, Xiaogang, 2024. "Prediction of bio-oil yield by machine learning model based on 'enhanced data' training," Renewable Energy, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:renene:v:225:y:2024:i:c:s0960148124002830
    DOI: 10.1016/j.renene.2024.120218
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124002830
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.120218?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. 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).
    2. Xiu, Shuangning & Shahbazi, Abolghasem, 2012. "Bio-oil production and upgrading research: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4406-4414.
    3. Onsree, Thossaporn & Tippayawong, Nakorn, 2021. "Machine learning application to predict yields of solid products from biomass torrefaction," Renewable Energy, Elsevier, vol. 167(C), pages 425-432.
    4. Leng, Lijian & Li, Tanghao & Zhan, Hao & Rizwan, Muhammad & Zhang, Weijin & Peng, Haoyi & Yang, Zequn & Li, Hailong, 2023. "Machine learning-aided prediction of nitrogen heterocycles in bio-oil from the pyrolysis of biomass," Energy, Elsevier, vol. 278(PB).
    5. Zhong, Dian & Zeng, Kuo & Li, Jun & Qiu, Yi & Flamant, Gilles & Nzihou, Ange & Vladimirovich, Vasilevich Sergey & Yang, Haiping & Chen, Hanping, 2022. "Characteristics and evolution of heavy components in bio-oil from the pyrolysis of cellulose, hemicellulose and lignin," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    6. Leng, Erwei & He, Ben & Chen, Jingwei & Liao, Gaoliang & Ma, Yinjie & Zhang, Feng & Liu, Shuai & E, Jiaqiang, 2021. "Prediction of three-phase product distribution and bio-oil heating value of biomass fast pyrolysis based on machine learning," Energy, Elsevier, vol. 236(C).
    Full references (including those not matched with items on IDEAS)

    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. Xiaorui Liu & Haiping Yang & Jiamin Yang & Fang Liu, 2022. "Application of Random Forest Model Integrated with Feature Reduction for Biomass Torrefaction," Sustainability, MDPI, vol. 14(23), pages 1-11, December.
    2. Potnuri, Ramesh & Suriapparao, Dadi V. & Sankar Rao, Chinta & Sridevi, Veluru & Kumar, Abhishankar, 2022. "Effect of dry torrefaction pretreatment of the microwave-assisted catalytic pyrolysis of biomass using the machine learning approach," Renewable Energy, Elsevier, vol. 197(C), pages 798-809.
    3. Aboagye, D. & Banadda, N. & Kiggundu, N. & Kabenge, I., 2017. "Assessment of orange peel waste availability in ghana and potential bio-oil yield using fast pyrolysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 814-821.
    4. Magdeldin, Mohamed & Kohl, Thomas & Järvinen, Mika, 2017. "Techno-economic assessment of the by-products contribution from non-catalytic hydrothermal liquefaction of lignocellulose residues," Energy, Elsevier, vol. 137(C), pages 679-695.
    5. Suopajärvi, Hannu & Pongrácz, Eva & Fabritius, Timo, 2013. "The potential of using biomass-based reducing agents in the blast furnace: A review of thermochemical conversion technologies and assessments related to sustainability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 511-528.
    6. Li, Chao & Li, Yuannian & Jiang, Yuchen & Zhang, Lijun & Zhang, Shu & Ding, Kuan & Li, Bin & Wang, Shuang & Hu, Xun, 2023. "Staged pyrolysis of biomass to probe the evolution of fractions of bio-oil," Energy, Elsevier, vol. 263(PD).
    7. Liang Zhu & Fangbin Wang & Jing Qi, 2024. "Washing walnut shells with the aqueous part of pyrolysis liquids: effect on biomass and pyrolysis product quality," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(11), pages 29169-29187, November.
    8. Yang, Zixu & Kumar, Ajay & Huhnke, Raymond L., 2015. "Review of recent developments to improve storage and transportation stability of bio-oil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 859-870.
    9. Juan Luis Aguirre & Juan Baena & María Teresa Martín & Leonor Nozal & Sergio González & José Luis Manjón & Manuel Peinado, 2020. "Composition, Ageing and Herbicidal Properties of Wood Vinegar Obtained through Fast Biomass Pyrolysis," Energies, MDPI, vol. 13(10), pages 1-17, May.
    10. Qarizada, Deana & Mohammadian, Erfan & Alias, Azil Bahari & Rahimi, Humapar Azhar, 2018. "Effect of temperature on bio-oil fractions of palm kernel shell thermal distillation," MPRA Paper 97687, University Library of Munich, Germany, revised 16 Apr 2018.
    11. Burov, Nikita O. & Savelenko, Vsevolod D. & Ershov, Mikhail A. & Vikhritskaya, Anastasia O. & Tikhomirova, Ekaterina O. & Klimov, Nikita A. & Kapustin, Vladimir M. & Chernysheva, Elena A. & Sereda, Al, 2023. "Knowledge contribution from science to technology in the conceptualization model to produce sustainable aviation fuels from lignocellulosic biomass," Renewable Energy, Elsevier, vol. 215(C).
    12. Okoye, P.U. & Hameed, B.H., 2016. "Review on recent progress in catalytic carboxylation and acetylation of glycerol as a byproduct of biodiesel production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 558-574.
    13. Beims, R.F. & Simonato, C.L. & Wiggers, V.R., 2019. "Technology readiness level assessment of pyrolysis of trygliceride biomass to fuels and chemicals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 521-529.
    14. 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.
    15. Wei, Rufei & Zhang, Lingling & Cang, Daqiang & Li, Jiaxin & Li, Xianwei & Xu, Chunbao Charles, 2017. "Current status and potential of biomass utilization in ferrous metallurgical industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 511-524.
    16. Saidi, Majid & Faraji, Mehdi, 2024. "Thermochemical conversion of neem seed biomass to sustainable hydrogen and biofuels: Experimental and theoretical evaluation," Renewable Energy, Elsevier, vol. 221(C).
    17. Leng, Lijian & Li, Tanghao & Zhan, Hao & Rizwan, Muhammad & Zhang, Weijin & Peng, Haoyi & Yang, Zequn & Li, Hailong, 2023. "Machine learning-aided prediction of nitrogen heterocycles in bio-oil from the pyrolysis of biomass," Energy, Elsevier, vol. 278(PB).
    18. Wang, Haoxiang & Liu, Jing, 2024. "Electrochemical corrosion study of carbon steel in bio-oil environments," Renewable Energy, Elsevier, vol. 221(C).
    19. Sun, Qie & Li, Hailong & Yan, Jinying & Liu, Longcheng & Yu, Zhixin & Yu, Xinhai, 2015. "Selection of appropriate biogas upgrading technology-a review of biogas cleaning, upgrading and utilisation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 521-532.
    20. Mariusz Wądrzyk & Marek Plata & Kamila Zaborowska & Rafał Janus & Marek Lewandowski, 2021. "Py-GC-MS Study on Catalytic Pyrolysis of Biocrude Obtained via HTL of Fruit Pomace," Energies, MDPI, vol. 14(21), pages 1-16, November.

    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:renene:v:225:y:2024:i:c:s0960148124002830. 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/renewable-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.