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Structure optimization and parameter identification of a downdraft biomass gasifier using combined method of numerical simulation and machine learning

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  • Hu, Yuyang
  • Yuan, Fuchun
  • Yin, Zhiqiang
  • Tian, Yuyang
  • Wang, Jiangjiang

Abstract

Downdraft gasifiers were commonly used in biomass gasification for low tar syngas production, with gasification performance highly influenced by structural designs and operating parameters. This study combines a computational fluid dynamics model with machine learning algorithms to systematically analyze the effects of the gasifying agent inlet vertical position, angle, type and four biomass feedstocks: wood pellets, wood chips, wheat straw, and furniture waste on the performance of a downdraft gasifier. Additionally, the influence of the gasifying agent mass flow rate, temperature, and biomass mass flow rate on H2 volume fraction and cold gas efficiency was analyzed. The results indicate that a vertical distance of 42.5 mm (from the center of the gasifying agent inlet to the upper surface of the throat), an inlet angle of 60°, and a gasifying agent composition of O2:H2O (20:80 by mass fraction), with furniture waste producing the highest H2 volume fraction (36.7 %), and wood pellets yielding the lowest. Wood chips demonstrated better gasification performance than wheat straw, achieving cold gas efficiency of 92.98 %. Parameter identification reveals that the gasifying agent flow rate shows the greatest influence on the H2 volume fraction, while biomass flow rate exhibits the highest influence to cold gas efficiency.

Suggested Citation

  • Hu, Yuyang & Yuan, Fuchun & Yin, Zhiqiang & Tian, Yuyang & Wang, Jiangjiang, 2026. "Structure optimization and parameter identification of a downdraft biomass gasifier using combined method of numerical simulation and machine learning," Renewable Energy, Elsevier, vol. 256(PA).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pa:s0960148125016325
    DOI: 10.1016/j.renene.2025.123968
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    References listed on IDEAS

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