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Optimizing H2 production from biomass: A machine learning-enhanced model of supercritical water gasification dynamics

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  • Huang, Chengwei
  • Xu, Jialing
  • Xu, Shuai
  • Shan, Murong
  • Liu, Shanke
  • Yu, Lijun

Abstract

Supercritical water gasification (SCWG) is recognized as an efficient technology for biomass conversion, demonstrating substantial potential in the sustainable energy sector. This paper focuses on the H2 production by SCWG of biomass. The objective is to develop an accurate gasification kinetic model that can facilitate the optimization of industrial reactor designs. A series of SCWG experiments is conducted in a batch reactor system under temperature of 500–600 °C and residence time of 1–20 min. Based on the experimental results, a hybrid-driven model for SCWG reaction is established. Firstly, experimental data is utilized to delineate SCWG reaction mechanistic model and forecast gas yields with an acceptable error margin. Subsequently, a Wasserstein Generative Adversarial Network Gradient Boosting Regression Grid Search (WGAN-GBR-GRID) model is applied to acquire knowledge of the predictive errors from the mechanistic model and to develop a hybrid model. The hybrid-driven model significantly enhances the average relative prediction error from 15.56 % to 0.02 % when compared to the mechanistic model. This result underscores the potential of the hybrid model in optimizing SCWG technology and the Shapley Additive exPlanations (SHAP) values in feature analysis shows the possible shortcomings of mechanistic model, thereby providing a new perspective for SCWG reaction dynamics modeling.

Suggested Citation

  • Huang, Chengwei & Xu, Jialing & Xu, Shuai & Shan, Murong & Liu, Shanke & Yu, Lijun, 2024. "Optimizing H2 production from biomass: A machine learning-enhanced model of supercritical water gasification dynamics," Energy, Elsevier, vol. 312(C).
    • Handle: RePEc:eee:energy:v:312:y:2024:i:c:s0360544224032663
    DOI: 10.1016/j.energy.2024.133490
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    as
    1. Shahbaz, Muhammad & Taqvi, Syed A. & Minh Loy, Adrian Chun & Inayat, Abrar & Uddin, Fahim & Bokhari, Awais & Naqvi, Salman Raza, 2019. "Artificial neural network approach for the steam gasification of palm oil waste using bottom ash and CaO," Renewable Energy, Elsevier, vol. 132(C), pages 243-254.
    2. Elmaz, Furkan & Yücel, Özgün & Mutlu, Ali Yener, 2020. "Predictive modeling of biomass gasification with machine learning-based regression methods," Energy, Elsevier, vol. 191(C).
    3. Zhang, Bowei & Guo, Simao & Jin, Hui, 2022. "Production forecast analysis of BP neural network based on Yimin lignite supercritical water gasification experiment results," Energy, Elsevier, vol. 246(C).
    4. Qi, Xingang & Li, Xujun & Liu, Fan & Lu, Libo & Jin, Hui & Wei, Wenwen & Chen, Yunan & Guo, Liejin, 2023. "Hydrogen production by kraft black liquor supercritical water gasification: Reaction pathway and kinetic," Energy, Elsevier, vol. 282(C).
    5. Ma, Zherui & Wang, Jiangjiang & Feng, Yingsong & Wang, Ruikun & Zhao, Zhenghui & Chen, Hongwei, 2023. "Hydrogen yield prediction for supercritical water gasification based on generative adversarial network data augmentation," Applied Energy, Elsevier, vol. 336(C).
    6. Santos J, J.M. & Zelioli, Í.A.M. & Guimarães F, E.É.X. & Freitas, A.C.D. & Mariano, A.P., 2024. "Supercritical water gasification thermodynamic study and hybrid modeling of machine learning with the ideal gas model: Application to gasification of microalgae biomass," Energy, Elsevier, vol. 291(C).
    7. Wang, Cui & Li, Linfeng & Chen, Yunan & Ge, Zhiwei & Jin, Hui, 2021. "Supercritical water gasification of wheat straw: Composition of reaction products and kinetic study," Energy, Elsevier, vol. 227(C).
    8. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    9. Akhtar, Javaid & Amin, Nor Aishah Saidina, 2011. "A review on process conditions for optimum bio-oil yield in hydrothermal liquefaction of biomass," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(3), pages 1615-1624, April.
    10. Yanli, Yang & Peidong, Zhang & Wenlong, Zhang & Yongsheng, Tian & Yonghong, Zheng & Lisheng, Wang, 2010. "Quantitative appraisal and potential analysis for primary biomass resources for energy utilization in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3050-3058, December.
    11. Huang, Zhiming & Bai, Yu & Chen, Jingwei & Wu, Xiaomin & E, Jiaqiang, 2024. "Molecular dynamics simulation on interaction effect of complex contents on supercritical water gasification of pig breeding wastewater for hydrogen production," Energy, Elsevier, vol. 294(C).
    12. Elmaz, Furkan & Yücel, Özgün, 2020. "Data-driven identification and model predictive control of biomass gasification process for maximum energy production," Energy, Elsevier, vol. 195(C).
    13. Wang, Cui & Hu, Cheng & Zheng, Yingzhe & Jin, Hui & Wu, Zhe, 2023. "Predictive control of reactor network model using machine learning for hydrogen-rich gas and biochar poly-generation by biomass waste gasification in supercritical water," Energy, Elsevier, vol. 282(C).
    14. Chen, Jingwei & Huang, Yizhen & Liu, Yang & Jiaqiang, E., 2024. "System development and thermodynamic performance analysis of a system integrating supercritical water gasification of black liquor with direct-reduced iron process," Energy, Elsevier, vol. 295(C).
    15. Xie, Xinyu & Wang, Xiaofang & Zhao, Pu & Hao, Yichen & Xie, Rong & Liu, Haitao, 2023. "Learning time-aware multi-phase flow fields in coal-supercritical water fluidized bed reactor with deep learning," Energy, Elsevier, vol. 263(PD).
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    More about this item

    Keywords

    Supercritical water gasification; H2 production; Reaction pathway; Kinetics modeling; Hybrid modeling;
    All these keywords.

    JEL classification:

    • H2 - Public Economics - - Taxation, Subsidies, and Revenue

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