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Reduction of greenhouse gases emissions by use of hydrogen produced in a refinery by water electrolysis

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  • Pivac, Ivan
  • Šimunović, Jakov
  • Barbir, Frano
  • Nižetić, Sandro

Abstract

The study considers production of hydrogen in a local oil refinery by electrolysis of water using exclusively electricity from renewable energy sources. Produced hydrogen is to be used as fuel for cars and buses. However, in case when the demand for hydrogen is less than production, which is expected in the beginning, as currently there are no hydrogen vehicles in Croatia, excess of produced green hydrogen would be used in the refinery production process, partially substituting hydrogen currently being produced from natural gas. The carbon footprint of hydrogen currently being produced in the refinery is calculated taking into account direct and indirect (upstream) CO2 emissions as well as methane fugitive emissions, along with the NOx emissions, converted to the amount of equivalent CO2 emissions. Each kg of green hydrogen used to power city buses instead of diesel would reduce well-to-wheels CO2 emissions by 33.39 kg, and when used to power cars instead of gasoline it would reduce them by 28.99 kg. If green hydrogen is used as a replacement for gray hydrogen produced from natural gas in the refinery, it would reduce the CO2 emissions by 21.74 kg per each kg of hydrogen substituted.

Suggested Citation

  • Pivac, Ivan & Šimunović, Jakov & Barbir, Frano & Nižetić, Sandro, 2024. "Reduction of greenhouse gases emissions by use of hydrogen produced in a refinery by water electrolysis," Energy, Elsevier, vol. 296(C).
    • Handle: RePEc:eee:energy:v:296:y:2024:i:c:s0360544224009307
    DOI: 10.1016/j.energy.2024.131157
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    References listed on IDEAS

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    1. Wang, Zhi & Yin, Yongbo & Yao, Guojia & Li, Kuangyu & Liu, Yang & Liu, Xuanqi & Tang, Zhenhao & Zhang, Fan & Peng, Xianyong & Lin, Jinxing & Zhu, Hang & Zhou, Huaichun, 2025. "BoilerNet: Deep reinforcement learning-based combustion optimization network for pulverized coal boiler," Energy, Elsevier, vol. 318(C).
    2. Mohammed H. Alshareef & Ayman F. Alghanmi, 2024. "Optimizing Maritime Energy Efficiency: A Machine Learning Approach Using Deep Reinforcement Learning for EEXI and CII Compliance," Sustainability, MDPI, vol. 16(23), pages 1-28, November.

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