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Machine learning prediction of biochar-specific surface area based on plant characterization information

Author

Listed:
  • Jiang, Zihao
  • Xia, Qi
  • Lu, Xueying
  • Yue, Wenjing
  • Chen, Aihui
  • Liu, Xiaogang
  • Chen, Juhui
  • Zhao, Chenxi

Abstract

Biochar has a wide range of environmental applications. The use of machine learning technology to predict the characteristics of biochar adsorption is a hot topic in current research. This paper proposes the concept of plant organs (PO) information for the first time. Based on a deep neural network (DNN) and light gradient boosting machine (LightGBM), a machine learning prediction model was created for the specific surface area of biochar, with 70 % of the 187 collected data points used as the training set and 30 % as the test set. The findings demonstrate that adding PO characteristics enhances the model's performance and accuracy, with the DNN model outperforming the LightGBM model. With the DNN model, the RMSE is lowered by 7.58, the MAE is reduced by 4.52, and the R2 increases from 0.897 to 0.917. Combined with Pearson correlation coefficient (PCC) analysis and Shapley additive explanations (SHAP) analysis, pyrolysis temperature (PT) is a key feature affecting the specific surface area of biochar, and PO significantly impacts the model's performance. By identifying and selecting specific plant organs for biochar production, researchers can more effectively optimize the biochar production process.

Suggested Citation

  • Jiang, Zihao & Xia, Qi & Lu, Xueying & Yue, Wenjing & Chen, Aihui & Liu, Xiaogang & Chen, Juhui & Zhao, Chenxi, 2025. "Machine learning prediction of biochar-specific surface area based on plant characterization information," Renewable Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:renene:v:243:y:2025:i:c:s0960148125002952
    DOI: 10.1016/j.renene.2025.122633
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    References listed on IDEAS

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    1. Muzyka, Roksana & Misztal, Edyta & Hrabak, Joanna & Banks, Scott W. & Sajdak, Marcin, 2023. "Various biomass pyrolysis conditions influence the porosity and pore size distribution of biochar," Energy, Elsevier, vol. 263(PE).
    2. Jia, Liangyuan & Shao, Wanyun & Wang, Jingjing & Qian, Yingying & Chen, Yingquan & Yang, Qingchun, 2024. "Machine learning-aided prediction of bio-BTX and olefins production from zeolite-catalyzed biomass pyrolysis," Energy, Elsevier, vol. 306(C).
    3. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(C).
    4. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
    5. Liu, Yang & Ao, Wenya & Fu, Jie & Siyal, Asif Ali & An, Qing & Zhou, Chunbao & Liu, Chenglong & Zhang, Yingwen & Chen, Zhiwen & Yun, Huimin & Dai, Jianjun & Bi, Xiaotao, 2024. "Microwave-assisted pyrolysis of industrial biomass waste: Insights into kinetic, characteristics and intrinsic mechanisms," Energy, Elsevier, vol. 306(C).
    6. Yibrah Gebreyesus & Damian Dalton & Sebastian Nixon & Davide De Chiara & Marta Chinnici, 2023. "Machine Learning for Data Center Optimizations: Feature Selection Using Shapley Additive exPlanation (SHAP)," Future Internet, MDPI, vol. 15(3), pages 1-17, February.
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