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Predicting hydrogen production from co-gasification of biomass and plastics using tree based machine learning algorithms

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  • Devasahayam, Sheila
  • Albijanic, Boris

Abstract

Hydrogen production from co-gasification of biomass and plastics are predicted using Machine Learning Algorithms, e.g., Decision tree and Ensemble methods. Independent variables are particle sizes of biomass and plastics, feedstock ratio and temperatures. The dependent variable is Hydrogen production. Model and prediction performances were evaluated/validated using model parameters. The relative importance scores for independent variables are RSS particle size > HDPE particle size > Temperature > Percent plastics. Size dependence of Hydrogen production indicated a surface-controlled reaction. Temperatures between 500 °C and 900 °C have less impact on H2 production compared to the size. Predictions were carried out using Train-test split, Cross-validation, and GridsearchCV model on the data unseen. Gradient Boosting performed the best.

Suggested Citation

  • Devasahayam, Sheila & Albijanic, Boris, 2024. "Predicting hydrogen production from co-gasification of biomass and plastics using tree based machine learning algorithms," Renewable Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:renene:v:222:y:2024:i:c:s0960148123017986
    DOI: 10.1016/j.renene.2023.119883
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    References listed on IDEAS

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    1. Yang, Ren-Xuan & Wu, Shan-Luo & Chuang, Kui-Hao & Wey, Ming-Yen, 2020. "Co-production of carbon nanotubes and hydrogen from waste plastic gasification in a two-stage fluidized catalytic bed," Renewable Energy, Elsevier, vol. 159(C), pages 10-22.
    2. Collard, François-Xavier & Blin, Joël, 2014. "A review on pyrolysis of biomass constituents: Mechanisms and composition of the products obtained from the conversion of cellulose, hemicelluloses and lignin," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 594-608.
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