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An innovative application of machine learning in prediction of the syngas properties of biomass chemical looping gasification based on extra trees regression algorithm

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  • Wang, Zhen
  • Mu, Lin
  • Miao, Hongchao
  • Shang, Yan
  • Yin, Hongchao
  • Dong, Ming

Abstract

Biomass chemical looping gasification (BCLG) is a promising carbon capture technology to produce hydrogen−rich syngas. In this study, the advanced machine learning method based on extra trees regression (ETR) algorithm was proposed to predict the syngas properties in the BCLG process. The prediction performance of the ETR algorithm was compared with traditional artificial neural network (ANN) and random forest (RF) algorithms. The outcomes demonstrated that the ETR algorithm had stronger prediction performance than RF and ANN algorithms in all target features with R2 > 0.88, which indicated the potential value of the ETR algorithm in small sample machine learning. Moreover, the results of elaborate feature importance analysis showed that the S/B ratio expressed a strong positive correlation with H2/CO ratio, and the most useful way to improve the gas yield and carbon conversion efficiency were reducing the λ value and increasing the temperature. The ETR algorithm was innovatively applied in the biomass field, which was beneficial to reduce the experiment consumption. It provided a comprehensive understanding of design and optimization of the commercial BCLG devices.

Suggested Citation

  • Wang, Zhen & Mu, Lin & Miao, Hongchao & Shang, Yan & Yin, Hongchao & Dong, Ming, 2023. "An innovative application of machine learning in prediction of the syngas properties of biomass chemical looping gasification based on extra trees regression algorithm," Energy, Elsevier, vol. 275(C).
    • Handle: RePEc:eee:energy:v:275:y:2023:i:c:s0360544223008320
    DOI: 10.1016/j.energy.2023.127438
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    References listed on IDEAS

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    1. Mutlu, Ali Yener & Yucel, Ozgun, 2018. "An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification," Energy, Elsevier, vol. 165(PA), pages 895-901.
    2. Li, Jie & Pan, Lanjia & Suvarna, Manu & Tong, Yen Wah & Wang, Xiaonan, 2020. "Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning," Applied Energy, Elsevier, vol. 269(C).
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