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Modeling, optimization, and mass-energy evaluation of a coupled hydrothermal carbonization-anaerobic digestion process assisted by machine learning

Author

Listed:
  • Wang, Ruikun
  • Hu, Junhui
  • Yang, Tao
  • Jia, Jiandong
  • Ma, Zherui
  • Tan, Shiteng
  • Zhao, Zhenghui
  • Yin, Qianqian

Abstract

Hydrothermal carbonization (HTC) coupled with anaerobic digestion (AD) enables the stepwise conversion of organic solid waste (OSW), thereby improving resource recovery and energy utilization efficiency. However, current studies on HTC-AD coupled processes lack systematic process modeling, making it difficult to support in-depth optimization and quantitative evaluation of process performance and resource recovery potential. To address this issue, this study integrates machine learning (ML) methods and reaction kinetics modeling to construct a process simulation model of the HTC-AD coupled system, aiming at process parameter optimization and resource recovery assessment. Using food waste (FW) as the feedstock, the optimal operating conditions obtained through multi-objective optimization are an HTC temperature of 220.32 °C, solid loading of 16.35 %, and residence time of 131.59 min. Under these conditions, the mass yield (MY) and higher heating value (HHV) of hydrochar reach 60.66 % and 23.36 MJ/kg, respectively, while the chemical oxygen demand (COD) of the liquid product reaches 45.20 g/L, indicating favorable potential for AD-based biogas production. At the optimal condition, 1 ton of FW can produce 110.18 kg of hydrochar and 13870.9 L of biogas, achieving a maximum net energy recovery of 1933.05 MJ, which demonstrates the considerable resource conversion potential of the coupled process. The results indicate that the model combines the advantages of data-driven ML and process-based simulation, making it suitable for process simulation and key parameter optimization of HTC-AD systems, with strong potential for practical application.

Suggested Citation

  • Wang, Ruikun & Hu, Junhui & Yang, Tao & Jia, Jiandong & Ma, Zherui & Tan, Shiteng & Zhao, Zhenghui & Yin, Qianqian, 2025. "Modeling, optimization, and mass-energy evaluation of a coupled hydrothermal carbonization-anaerobic digestion process assisted by machine learning," Energy, Elsevier, vol. 337(C).
    • Handle: RePEc:eee:energy:v:337:y:2025:i:c:s0360544225043919
    DOI: 10.1016/j.energy.2025.138749
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