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Predictive single-step kinetic model of biomass devolatilization for CFD applications: A comparison study of empirical correlations (EC), artificial neural networks (ANN) and random forest (RF)

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  • Xing, Jiangkuan
  • Wang, Haiou
  • Luo, Kun
  • Wang, Shuai
  • Bai, Yun
  • Fan, Jianren

Abstract

The single-step model has been widely used for devolatilization in the computational fluid dynamics (CFD) of biomass gasification and combustion due to its low computational cost. The kinetic parameters of the single-step model are just obtained from previous studies and kept constant without regarding to the effects of biomass types and heating conditions, resulting in an obvious deviation on the treatment of biomass devolatilization. Here, several models, including the empirical correlations (EC), artificial neural networks (ANN) and random forest (RF) models, are developed to predict the kinetic parameters of the single-step model for CFD applications based on the biomass chemical compositions and heating conditions, and their performances are compared to highlight the optimal model. Two biomass devolatilization databases, used for training and validation respectively, are constructed from available experiments in the literature. The kinetic parameters are then fit from the database. The training and validation results show the EC model provides a poor performance with the lowest determination coefficients (R2≤0.80), while the ANN and RF models show an obviously better performance, with the ANN giving the middle performance (R2≥0.84) and the RF model giving the best performance (R2≥0.92). The variable importance measurement (VIM) results are also presented and discussed.

Suggested Citation

  • Xing, Jiangkuan & Wang, Haiou & Luo, Kun & Wang, Shuai & Bai, Yun & Fan, Jianren, 2019. "Predictive single-step kinetic model of biomass devolatilization for CFD applications: A comparison study of empirical correlations (EC), artificial neural networks (ANN) and random forest (RF)," Renewable Energy, Elsevier, vol. 136(C), pages 104-114.
    • Handle: RePEc:eee:renene:v:136:y:2019:i:c:p:104-114
    DOI: 10.1016/j.renene.2018.12.088
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    1. Ly, Hoang Vu & Kim, Jinsoo & Kim, Seung-Soo, 2013. "Pyrolysis characteristics and kinetics of palm fiber in a closed reactor," Renewable Energy, Elsevier, vol. 54(C), pages 91-95.
    2. Kwietniewska, Ewa & Tys, Jerzy, 2014. "Process characteristics, inhibition factors and methane yields of anaerobic digestion process, with particular focus on microalgal biomass fermentation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 491-500.
    3. Cao, Fei & Li, Huashan & Yang, Tian & Li, Yan & Zhu, Tianyu & Zhao, Liang, 2017. "Evaluation of diffuse solar radiation models in Northern China: New model establishment and radiation sources comparison," Renewable Energy, Elsevier, vol. 103(C), pages 708-720.
    4. Van de Velden, Manon & Baeyens, Jan & Brems, Anke & Janssens, Bart & Dewil, Raf, 2010. "Fundamentals, kinetics and endothermicity of the biomass pyrolysis reaction," Renewable Energy, Elsevier, vol. 35(1), pages 232-242.
    5. Couto, Nuno & Silva, Valter & Monteiro, Eliseu & Brito, Paulo & Rouboa, Abel, 2015. "Using an Eulerian-granular 2-D multiphase CFD model to simulate oxygen air enriched gasification of agroindustrial residues," Renewable Energy, Elsevier, vol. 77(C), pages 174-181.
    6. 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.
    7. Bach, Quang-Vu & Tran, Khanh-Quang & Skreiberg, Øyvind, 2017. "Combustion kinetics of wet-torrefied forest residues using the distributed activation energy model (DAEM)," Applied Energy, Elsevier, vol. 185(P2), pages 1059-1066.
    8. Masmoudi, Mohamed Ali & Sahraoui, Melik & Grioui, Najla & Halouani, Kamel, 2014. "2-D Modeling of thermo-kinetics coupled with heat and mass transfer in the reduction zone of a fixed bed downdraft biomass gasifier," Renewable Energy, Elsevier, vol. 66(C), pages 288-298.
    9. Hosseinpour, Soleiman & Aghbashlo, Mortaza & Tabatabaei, Meisam & Mehrpooya, Mehdi, 2017. "Estimation of biomass higher heating value (HHV) based on the proximate analysis by using iterative neural network-adapted partial least squares (INNPLS)," Energy, Elsevier, vol. 138(C), pages 473-479.
    10. Goyal, H.B. & Seal, Diptendu & Saxena, R.C., 2008. "Bio-fuels from thermochemical conversion of renewable resources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(2), pages 504-517, February.
    11. Ma, Jun & Cheng, Jack C.P., 2016. "Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests," Applied Energy, Elsevier, vol. 183(C), pages 193-201.
    Full references (including those not matched with items on IDEAS)

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