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A combined approach-based techno-economic-environmental multi-optimization of a hydrogen generation system through waste biomass air-steam gasification

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  • Tavakoli, Navid
  • Khoshkenar, Payam
  • Pourfayaz, Fathollah

Abstract

In this study, a biomass air-steam gasification system for hydrogen production was simulated in Aspen Plus and validated using experimental studies. Sewage sludge was considered as the biomass feed. A combined approach including the combination of the NSGA-II algorithm, the response surface methodology (RSM) and TOPSIS decision-making method, was used to multi-optimize this hydrogen production system. To optimize the performance of the system, first, mathematical equations between equivalence ratio (ER), steam to biomass ratio (SBR), and gasifier temperature as the decision variables and the mole flow of H2 and CO2 and the levelized cost of hydrogen (LCOH) as the objective functions were obtained by the RSM technique. To maximize the mole flow of H2 and minimize CO2 emission and LCOH, a multi-objective optimization using NSGA-II was conducted. The ER, SBR and gasifier temperature were analyzed in the range of 0.1–0.4, 0.2 to 2 and 600 °C–1000 °C, respectively. By employing the TOPSIS decision-making method, several optimum points were obtained by giving each of the decision variables a specific weight. The lowest LCOH was found to be 1.5 €/kg H2 at the ER of 0.103, SBR of 0.83, and gasification temperature of 956 °C.

Suggested Citation

  • Tavakoli, Navid & Khoshkenar, Payam & Pourfayaz, Fathollah, 2024. "A combined approach-based techno-economic-environmental multi-optimization of a hydrogen generation system through waste biomass air-steam gasification," Renewable Energy, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:renene:v:225:y:2024:i:c:s0960148124003240
    DOI: 10.1016/j.renene.2024.120259
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    1. Song, Hao & Xia, Jiageng & Hu, Qiang & Cheng, Wei & Yang, Yang & Chen, Hanping & Yang, Haiping, 2024. "Comprehensive experimental assessment of biomass steam gasification with different types: correlation and multiple linear regression analysis with feedstock characteristics," Renewable Energy, Elsevier, vol. 237(PA).

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