IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v281y2023ics0360544223015372.html
   My bibliography  Save this article

Slurry-ability mathematical modeling of microwave-modified lignite: A comparative analysis of multivariate non-linear regression model and XGBoost algorithm model

Author

Listed:
  • Ren, Yangguang
  • Lv, Ziqi
  • Xu, Zhiqiang
  • Wang, Qun
  • Wang, Zhe

Abstract

In the preparation of microwave-modified lignite (ML) water slurry (MLWS), its specific surface area, oxygen functional groups and oxygen-carbon atomic ratio are important factors affecting the slurry ability. Based on the relationship between the physicochemical properties of 23 ML samples and their maximum solid concentrations, a multivariate nonlinear regression (MNR) model and a machine learning model based on XGBoost algorithm were established to predict the slurry ability. The determination coefficient (R2), the mean average error (MAE) and the mean squared errors (MSE) for MNR mode is 0.928, 0.507 and 0.391, respectively. The R2, MAE and MSE for XGBoost model is 0.582, 0.619 and 0.667, respectively. Hence, the MNR model with less error percentage has a higher accuracy than the XGBoost model, but the XGBoost model is highly efficient, flexible and portable with strong generalization ability to unknown data. Overall, the proposed models in this study have a good effect on the prediction of the slurry ability of ML, and serve as a technical reference for future predicting the slurry ability of other types of coal.

Suggested Citation

  • Ren, Yangguang & Lv, Ziqi & Xu, Zhiqiang & Wang, Qun & Wang, Zhe, 2023. "Slurry-ability mathematical modeling of microwave-modified lignite: A comparative analysis of multivariate non-linear regression model and XGBoost algorithm model," Energy, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:energy:v:281:y:2023:i:c:s0360544223015372
    DOI: 10.1016/j.energy.2023.128143
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223015372
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128143?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. Akdeniz, Fikret & Biçil, Metin & Karadede, Yusuf & Özbek, Füreya Elif & Özdemir, Gültekin, 2018. "Application of real valued genetic algorithm on prediction of higher heating values of various lignocellulosic materials using lignin and extractive contents," Energy, Elsevier, vol. 160(C), pages 1047-1054.
    2. Zhang, Yishuo & Li, Gang & Muskat, Birgit & Law, Rob & Yang, Yating, 2020. "Group pooling for deep tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 82(C).
    3. Ren, Yangguang & Xu, Zhiqiang & Gu, Suqian, 2022. "Physicochemical properties and slurry ability changes of lignite after microwave upgrade with the assist of lignite semi-coke," Energy, Elsevier, vol. 252(C).
    4. Ullah, Habib & Liu, Guijian & Yousaf, Balal & Ali, Muhammad Ubaid & Abbas, Qumber & Zhou, Chuncai & Rashid, Audil, 2018. "Hydrothermal dewatering of low-rank coals: Influence on the properties and combustion characteristics of the solid products," Energy, Elsevier, vol. 158(C), pages 1192-1203.
    5. Li, Dedi & Liu, Jianzhong & Wang, Shuangni & Cheng, Jun, 2020. "Study on coal water slurries prepared from coal chemical wastewater and their industrial application," Applied Energy, Elsevier, vol. 268(C).
    6. Gu, Suqian & Xu, Zhiqiang & Ren, Yangguang & Tu, Yanan & Sun, Meijie & Liu, Xiangyang, 2021. "An approach for upgrading lignite to improve slurryability: Blending with direct coal liquefaction residue under microwave-assisted pyrolysis," Energy, Elsevier, vol. 222(C).
    7. Mei Zhang & Wanli Chen & Jun Yin & Tao Feng, 2022. "Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost," Energies, MDPI, vol. 15(16), pages 1-18, August.
    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. Ren, Yangguang & Xu, Zhiqiang & Gu, Suqian, 2022. "Physicochemical properties and slurry ability changes of lignite after microwave upgrade with the assist of lignite semi-coke," Energy, Elsevier, vol. 252(C).
    2. Zhu, Hongqing & Liao, Qi & Qu, Baolin & Hu, Lintao & Wang, Haoran & Gao, Rongxiang & Zhang, Yilong, 2023. "Relationship between the main functional groups and complex permittivity in pre-oxidised lignite at terahertz frequencies based on grey correlation analysis," Energy, Elsevier, vol. 278(C).
    3. Al-Falahi, Monaaf D.A. & Jayasinghe, Shantha D.G. & Enshaei, Hossein, 2019. "Hybrid algorithm for optimal operation of hybrid energy systems in electric ferries," Energy, Elsevier, vol. 187(C).
    4. Pan, Rui & Liu, Tongshen & Huang, Wei & Wang, Yuxin & Yang, Duo & Chen, Jie, 2023. "State of health estimation for lithium-ion batteries based on two-stage features extraction and gradient boosting decision tree," Energy, Elsevier, vol. 285(C).
    5. Hong, Jichao & Li, Kerui & Liang, Fengwei & Yang, Haixu & Zhang, Chi & Yang, Qianqian & Wang, Jiegang, 2024. "A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks," Energy, Elsevier, vol. 289(C).
    6. Emrah Kocak & Fevzi Okumus & Mehmet Altin, 2023. "Global pandemic uncertainty, pandemic discussion and visitor behaviour: A comparative tourism demand estimation for the US," Tourism Economics, , vol. 29(5), pages 1225-1250, August.
    7. Gao, Mingqiang & Cheng, Cheng & Miao, Zhenyong & Wan, Keji & He, Qiongqiong, 2023. "Physicochemical properties, combustion kinetics and thermodynamics of oxidized lignite," Energy, Elsevier, vol. 268(C).
    8. Vadim Dorokhov & Geniy Kuznetsov & Galina Nyashina, 2022. "Combustion of Coal and Coal Slime in Steam-Air Environment and in Slurry Form," Energies, MDPI, vol. 15(24), pages 1-23, December.
    9. Shi, Jingxin & Huang, Wenping & Han, Hongjun & Xu, Chunyan, 2021. "Pollution control of wastewater from the coal chemical industry in China: Environmental management policy and technical standards," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    10. Salim Jibrin Danbatta & Asaf Varol, 2022. "ANN–polynomial–Fourier series modeling and Monte Carlo forecasting of tourism data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 920-932, August.
    11. Mao, Lirui & Zheng, Mingdong & Li, Hanxu, 2023. "Acceleration effect of BDO tar on coal water slurry during co-gasification," Energy, Elsevier, vol. 262(PA).
    12. Zhang, Nan & Wang, Guangwei & Yu, Chunmei & Zhang, Jianliang & Dang, Han & Zhang, Cuiliu & Ning, Xiaojun & Wang, Chuan, 2022. "Physicochemical structure characteristics and combustion kinetics of low-rank coal by hydrothermal carbonization," Energy, Elsevier, vol. 238(PA).
    13. Jian-Wu Bi & Tian-Yu Han & Yanbo Yao, 2024. "Collaborative forecasting of tourism demand for multiple tourist attractions with spatial dependence: A combined deep learning model," Tourism Economics, , vol. 30(2), pages 361-388, March.
    14. Bruno Esteves & Umut Sen & Helena Pereira, 2023. "Influence of Chemical Composition on Heating Value of Biomass: A Review and Bibliometric Analysis," Energies, MDPI, vol. 16(10), pages 1-17, May.
    15. Haodong Sun & Yang Yang & Yanyan Chen & Xiaoming Liu & Jiachen Wang, 2023. "Tourism demand forecasting of multi-attractions with spatiotemporal grid: a convolutional block attention module model," Information Technology & Tourism, Springer, vol. 25(2), pages 205-233, June.
    16. Pegoretti Leite de Souza, Hector Jesus & Muñoz, Fernando & Mendonça, Regis Teixeira & Sáez, Katia & Olave, Rodrigo & Segura, Cristina & de Souza, Daniel P.L. & de Paula Protásio, Thiago & Rodríguez-So, 2021. "Influence of lignin distribution, physicochemical characteristics and microstructure on the quality of biofuel pellets made from four different types of biomass," Renewable Energy, Elsevier, vol. 163(C), pages 1802-1816.
    17. Bi, Jian-Wu & Li, Hui & Fan, Zhi-Ping, 2021. "Tourism demand forecasting with time series imaging: A deep learning model," Annals of Tourism Research, Elsevier, vol. 90(C).
    18. Byun, Manhee & Lim, Dongjun & Lee, Boreum & Kim, Ayeon & Lee, In-Beum & Brigljević, Boris & Lim, Hankwon, 2022. "Economically feasible decarbonization of the Haber-Bosch process through supercritical CO2 Allam cycle integration," Applied Energy, Elsevier, vol. 307(C).
    19. Konstantin Osintsev & Sergei Aliukov & Anatoliy Alabugin, 2022. "A Review of Methods, and Analytical and Experimental Studies on the Use of Coal–Water Suspensions," Mathematics, MDPI, vol. 10(20), pages 1-25, October.
    20. Zhang, Wei & Wang, Deming & Xin, Haihui & Wang, Chenguang & Xu, Zuoming & Hou, Zhenhai & Qi, Zhangfan, 2024. "Reignition characteristics of lignite affected by pre-oxidation and liquid nitrogen cold soaking," Energy, Elsevier, vol. 303(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:eee:energy:v:281:y:2023:i:c:s0360544223015372. 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.