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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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