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Renewable hydrogen production from the organic fraction of municipal solid waste through a novel carbon-negative process concept

Author

Listed:
  • Wang, Shule
  • Yang, Hanmin
  • Shi, Ziyi
  • Zaini, Ilman Nuran
  • Wen, Yuming
  • Jiang, Jianchun
  • Jönsson, Pär Göran
  • Yang, Weihong

Abstract

Bioenergy with carbon capture and storage (BECCS) is one of the prevailing negative carbon emission technologies. Ensuring a hydrogen economy is essential to achieving the carbon-neutral goal. In this regard, the present study contributed by proposing a carbon negative process for producing high purity hydrogen from the organic fraction of municipal solid waste (OFMSW). This integrated process comprises anaerobic digestion, pyrolysis, catalytic reforming, water-gas shift, and pressure swing adsorption technologies. By focusing on Sweden, the proposed process was developed and evaluated through sensitivity analysis, mass and energy balance calculations, techno-economic assessment, and practical feasibility analysis. By employing the optimum operating conditions from the sensitivity analysis, 72.2 kg H2 and 701.47 kg negative CO2 equivalent emissions were obtained by treating 1 ton of dry OFMSW. To achieve these results, 6621.4 MJ electricity and 325 kg of steam were utilized during this process. Based on this techno-economic assessment of implementing the proposed process in Stockholm, when the negative CO2 equivalent emissions are recognized as income, the internal rate of return and the discounted payback period can be obtained as 26% and 4.3 years, respectively. Otherwise, these values will be 13% and 7.2 years.

Suggested Citation

  • Wang, Shule & Yang, Hanmin & Shi, Ziyi & Zaini, Ilman Nuran & Wen, Yuming & Jiang, Jianchun & Jönsson, Pär Göran & Yang, Weihong, 2022. "Renewable hydrogen production from the organic fraction of municipal solid waste through a novel carbon-negative process concept," Energy, Elsevier, vol. 252(C).
  • Handle: RePEc:eee:energy:v:252:y:2022:i:c:s0360544222009598
    DOI: 10.1016/j.energy.2022.124056
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    2. Wang, Zhengxin & Peng, Xinggan & Xia, Ao & Shah, Akeel A. & Yan, Huchao & Huang, Yun & Zhu, Xianqing & Zhu, Xun & Liao, Qiang, 2023. "Comparison of machine learning methods for predicting the methane production from anaerobic digestion of lignocellulosic biomass," Energy, Elsevier, vol. 263(PD).
    3. Zhang, Zhixiang & Yuan, Han & Mei, Ning, 2023. "Theoretical analysis on extraction-ejection combined power and refrigeration cycle for ocean thermal energy conversion," Energy, Elsevier, vol. 273(C).

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