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Estimation of syngas yield in hydrothermal gasification process by application of artificial intelligence models

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  • Ayub, Yousaf
  • Hu, Yusha
  • Ren, Jingzheng

Abstract

Quality syngas production with higher moles of hydrogen and methane are the primary objective of gasification process which is dependent upon the process parameters and composition of biomass. However, it is always a costly and time-consuming task to get the optimum biomass composition and process parameters for quality syngas production. In this research, artificial intelligence (AI) algorithms have been applied for high quality syngas prediction with better moles fractions of hydrogen and methane using hydrothermal gasification (HTG). Comparative analysis of Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Gradient Boost Regressor (GBR), Extreme Boost Regressor (XGB), and Random Forest Regressor (RFR) based algorithms have been done to select an optimal one. Ultimate analysis of biomass and process input parameters inlcuding temperature, pressure, percentage solid content of biomass, and resident time have been used as an input parameter for prediction models. Final comparative results of these AI models conclude that XGB has a better prediction result as compared to other with coefficient of determinant (R2) and mean square errors ranges from 0.85 to 0.95 and 0.008–0.01, respectively. Furthermore, process temperature and the resident time are the most contributing factors in mole fractions of hydrogen and methane. Higher hydrogen and oxygen contents in the biomass, significantly contributes to the production of quality syngas.

Suggested Citation

  • Ayub, Yousaf & Hu, Yusha & Ren, Jingzheng, 2023. "Estimation of syngas yield in hydrothermal gasification process by application of artificial intelligence models," Renewable Energy, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:renene:v:215:y:2023:i:c:s0960148123008595
    DOI: 10.1016/j.renene.2023.118953
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    Cited by:

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    2. Qi, Jingwei & Wang, Yijie & Xu, Pengcheng & Hu, Ming & Huhe, Taoli & Ling, Xiang & Yuan, Haoran & Li, Jiadong & Chen, Yong, 2024. "Biomass hydrothermal gasification characteristics study: based on deep learning for data generation and screening strategies," Energy, Elsevier, vol. 312(C).
    3. Mu, Lin & Wang, Zhen & Sun, Meng & Shang, Yan & Pu, Hang & Dong, Ming, 2024. "Machine learning model with a novel self–adjustment method: A powerful tool for predicting biomass ash fusibility and enhancing its potential applications," Renewable Energy, Elsevier, vol. 237(PA).
    4. Ayub, Yousaf & Ren, Jingzheng & He, Chang, 2024. "Unlocking waste potential: A neural network approach to forecasting sustainable acetaldehyde production from ethanol upcycling in biomass waste gasification," Energy, Elsevier, vol. 299(C).
    5. Song, Hao & Xia, Jiageng & Hu, Qiang & Cheng, Wei & Yang, Yang & Chen, Hanping & Yang, Haiping, 2024. "Comprehensive experimental assessment of biomass steam gasification with different types: correlation and multiple linear regression analysis with feedstock characteristics," Renewable Energy, Elsevier, vol. 237(PA).
    6. Yan Zhang & Kai Yue & Chang Yuan & Jiahao Xiang, 2025. "A Data Reconciliation-Based Method for Performance Estimation of Entrained-Flow Pulverized Coal Gasification," Energies, MDPI, vol. 18(5), pages 1-22, February.
    7. Ayub, Yousaf & Zhou, Jianzhao & Shen, Weifeng & Ren, Jingzheng, 2023. "Innovative valorization of biomass waste through integration of pyrolysis and gasification: Process design, optimization, and multi-scenario sustainability analysis," Energy, Elsevier, vol. 282(C).
    8. Olca, Kadriye Deniz & Yücel, Özgün, 2024. "Unveiling the potential of operating time in improving machine learning models’ performance for waste biomass gasification systems," Renewable Energy, Elsevier, vol. 237(PA).

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