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Developing a fuzzy-model with particle swarm optimization-based for improving the conversion and gasification rate of palm kernel shell

Author

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  • Nassef, Ahmed M.
  • Sayed, Enas T.
  • Rezk, Hegazy
  • Inayat, Abrar
  • Yousef, Bashria A.A.
  • Abdelkareem, Mohammad A.
  • Olabi, A.G.

Abstract

Biomass steam gasification is a promising technology for syngas production. This study focused on the effect of different gasification parameters like temperature (°C), CaO/biomass, particle size, and coal bottom ash (wt%), on the conversion and gasification rate via steam gasification using palm kernel shell (PKS) as feedstock. The essential goal is to maximize the conversion and gasification rates of the PKS steam gasification system by deciding the optimum operating parameters using particle swarm optimization. The PKS gasification model was developed using a fuzzy logic approach. Gasification temperature, coal bottom ash percentage, CaO/biomass ratio, and PKS particle size; are the four variables of decisions used in the optimization process. The findings were compared with the experimental data at optimal conditions and those expected via the methodology of response surface (RSM) approach. The proposed strategy provided optimum conditions of a temperature of 750 °C, the particle size of 0.5 mm, CaO/biomass ratio of 1.36, and coal bottom ash of 0.0337 wt%, for a conversion rate of 89.25%, and a gasification rate of 85.51%, which is 10% higher than those obtained experimentally. The current research work was able to predict the optimal conditions for the enhancement of conversion and gasification rate.

Suggested Citation

  • Nassef, Ahmed M. & Sayed, Enas T. & Rezk, Hegazy & Inayat, Abrar & Yousef, Bashria A.A. & Abdelkareem, Mohammad A. & Olabi, A.G., 2020. "Developing a fuzzy-model with particle swarm optimization-based for improving the conversion and gasification rate of palm kernel shell," Renewable Energy, Elsevier, vol. 166(C), pages 125-135.
    • Handle: RePEc:eee:renene:v:166:y:2020:i:c:p:125-135
    DOI: 10.1016/j.renene.2020.11.037
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    2. Fathy, Ahmed & Ferahtia, Seydali & Rezk, Hegazy & Yousri, Dalia & Abdelkareem, Mohammad Ali & Olabi, A.G., 2022. "Optimal adaptive fuzzy management strategy for fuel cell-based DC microgrid," Energy, Elsevier, vol. 247(C).

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