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

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  • 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
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    References listed on IDEAS

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    1. 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).
    2. 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.
    3. 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).
    4. 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).
    5. 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).
    6. 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.
    7. 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.
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