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<p>Accurate characterization of heavy petroleum fractions remains challenging in hydroprocessing reactor modeling, where reliable hydrogen-solubility estimates in complex hydrocarbon mixtures are essential. Continuous thermodynamics has been successfully applied to represent crude oils and petroleum fractions in vapour–liquid equilibrium calculations through continuous composition distributions. Building on this framework, we propose a continuous-thermodynamics-based characterization methodology for vacuum gas oils and integrate it with the Augmented Grayson–Streed (AGS) approach to predict hydrogen solubility. Over temperature and pressure ranges of 459–653&#xa0;K and 1.0–12.5&#xa0;MPa, respectively, the proposed strategy reduces the global average absolute deviation with respect to experimental data from 22.5% to 11%. Beyond improving accuracy, the framework provides a systematic route to define a minimal set of pseudocomponents, reducing arbitrariness in heavy-fraction characterization while relying only on routinely measured laboratory data.</p>

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Continuous thermodynamics approach for hydrogen solubility prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月9日

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  • Wilfredo Angulo,
  • María G. Lucena,
  • Yris González,
  • Dany De Cecchis,
  • Alexander Espinoza,
  • Juan P. Requez,
  • Daniela Galatro

摘要

Accurate characterization of heavy petroleum fractions remains challenging in hydroprocessing reactor modeling, where reliable hydrogen-solubility estimates in complex hydrocarbon mixtures are essential. Continuous thermodynamics has been successfully applied to represent crude oils and petroleum fractions in vapour–liquid equilibrium calculations through continuous composition distributions. Building on this framework, we propose a continuous-thermodynamics-based characterization methodology for vacuum gas oils and integrate it with the Augmented Grayson–Streed (AGS) approach to predict hydrogen solubility. Over temperature and pressure ranges of 459–653 K and 1.0–12.5 MPa, respectively, the proposed strategy reduces the global average absolute deviation with respect to experimental data from 22.5% to 11%. Beyond improving accuracy, the framework provides a systematic route to define a minimal set of pseudocomponents, reducing arbitrariness in heavy-fraction characterization while relying only on routinely measured laboratory data.