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A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification

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  • Li, Jie
  • Suvarna, Manu
  • Pan, Lanjia
  • Zhao, Yingru
  • Wang, Xiaonan

Abstract

Recent advances in machine learning (ML) have witnessed a profound interest and application in the domain of waste to energy. However, their black-box nature renders challenges for ubiquitous acceptance. To address this issue, we developed a novel and first-of-its-kind hybrid data-driven and mechanistic modelling approach for hydrothermal gasification (HTG) of wet waste, in which a gradient boost regressor (GBR) integrated optimization model was first developed to predict and optimize the yield of syngas from HTG of wet waste, and then the predictions of the GBR model were validated and interpreted via mechanistic simulations in Aspen Plus. Results showed that the GBR model had a prediction performance with test R2 > 0.90. GBR-based feature analysis identified that reaction temperature and feedstock solid content were the two significant features necessary to achieve high H2 yield in syngas. Moreover, the GBR-based optimization provided optimal process conditions for H2-rich syngas production, which was validated over mechanistic simulations in Aspen Plus with an error of less than 20%. Interpretation from the mechanistic simulation revealed that steam and dry methane reforming and CO2 methanation were the most significant reactions in the overall HTG process, responsible to produce H2-rich syngas. Integrated modelling approach as presented in this study shows how data-driven and mechanistic models complement each other and can aid the acceleration of experimental design of HTG by ML in general.

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  • Li, Jie & Suvarna, Manu & Pan, Lanjia & Zhao, Yingru & Wang, Xiaonan, 2021. "A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification," Applied Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:appene:v:304:y:2021:i:c:s0306261921010345
    DOI: 10.1016/j.apenergy.2021.117674
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    1. Suvarna, Manu & Jahirul, Mohammad Islam & Aaron-Yeap, Wai Hung & Augustine, Cheryl Valencia & Umesh, Anushri & Rasul, Mohammad Golam & Günay, Mehmet Erdem & Yildirim, Ramazan & Janaun, Jidon, 2022. "Predicting biodiesel properties and its optimal fatty acid profile via explainable machine learning," Renewable Energy, Elsevier, vol. 189(C), pages 245-258.
    2. Ascher, Simon & Sloan, William & Watson, Ian & You, Siming, 2022. "A comprehensive artificial neural network model for gasification process prediction," Applied Energy, Elsevier, vol. 320(C).
    3. Shi, Tao & Zhou, Jianzhao & Ren, Jingzheng & Ayub, Yousaf & Yu, Haoshui & Shen, Weifeng & Li, Qiao & Yang, Ao, 2023. "Co-valorisation of sewage sludge and poultry litter waste for hydrogen production: Gasification process design, sustainability-oriented optimization, and systematic assessment," Energy, Elsevier, vol. 272(C).
    4. Li, Jie & Yu, Di & Pan, Lanjia & Xu, Xinhai & Wang, Xiaonan & Wang, Yin, 2023. "Recent advances in plastic waste pyrolysis for liquid fuel production: Critical factors and machine learning applications," Applied Energy, Elsevier, vol. 346(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. Liu, Shanke & Yang, Yan & Yu, Lijun & Cao, Yu & Liu, Xinyi & Yao, Anqi & Cao, Yaping, 2023. "Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation," Energy, Elsevier, vol. 272(C).

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