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<p>Helium, a non-renewable strategic resource, demands efficient purification technologies to safeguard national resource security. To address the technical challenges of helium extraction from liquefied natural gas boil-off gas (BOG), this study developed a novel pressure swing adsorption (PSA) process using Aspen Adsorption software. Through systematic adsorbent screening, the CMS-5000-A carbon molecular sieves (CMS) with a pore size range<?ColorInfoStart FFFFFF-Background1?><sup>z,2</sup><?ColorInfoEnd FFFFFF-Background1?> of 0.5 ~ 0.7&#xa0;nm was selected, whose surface oxygen-containing polar functional groups enhanced interactions with the quadrupole moment of N<sub>2</sub>, achieving an exceptional N<sub>2</sub>/He adsorption selectivity of 87.5:1. A multi-scale coupled model integrating mass, energy, and momentum conservation equations was established to systematically investigate the effects of operational parameters on separation performance. An extended two-column PSA scheme incorporating pressure equalization and vacuum desorption steps was proposed. For the first time, Bayesian algorithm-enabled multi-parameter co-optimization of six key operational parameters (feed flow rate, pressure, bed height, porosity, packing density, temperature) was realized, overcoming the limitations of traditional single-factor optimization in handling parameter coupling effects. Bayesian algorithm-enabled multi-parameter optimization revealed that an adsorption pressure of 0.3 ~ 0.4&#xa0;MPa increased helium recovery by 15%, while a 1.0 ~ 1.1&#xa0;m adsorption bed height ensured product purity ≥ 99.999% with energy consumption maintained at 0.36 kWh/m<sup>3</sup>. Compared to the single-column system, the two-column configuration improved helium recovery by 5.06%. Notably, the novel PSA process reduces energy consumption by up to 60% compared to conventional methods, providing a data-driven and quantifiable design paradigm for lean helium purification and presenting an efficient and energy-saving technical solution for industrial-scale helium purification.</p>

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Application of an Intelligent Optimization Method Based on Aspen Adsorption in the Purification Process of Helium Via Pressure Swing Adsorption

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Hong-nan Lin,
  • Zheng Fan,
  • Gen-hui Jing,
  • Han-lei Zhao,
  • Yi-han Li

摘要

Helium, a non-renewable strategic resource, demands efficient purification technologies to safeguard national resource security. To address the technical challenges of helium extraction from liquefied natural gas boil-off gas (BOG), this study developed a novel pressure swing adsorption (PSA) process using Aspen Adsorption software. Through systematic adsorbent screening, the CMS-5000-A carbon molecular sieves (CMS) with a pore size rangez,2 of 0.5 ~ 0.7 nm was selected, whose surface oxygen-containing polar functional groups enhanced interactions with the quadrupole moment of N2, achieving an exceptional N2/He adsorption selectivity of 87.5:1. A multi-scale coupled model integrating mass, energy, and momentum conservation equations was established to systematically investigate the effects of operational parameters on separation performance. An extended two-column PSA scheme incorporating pressure equalization and vacuum desorption steps was proposed. For the first time, Bayesian algorithm-enabled multi-parameter co-optimization of six key operational parameters (feed flow rate, pressure, bed height, porosity, packing density, temperature) was realized, overcoming the limitations of traditional single-factor optimization in handling parameter coupling effects. Bayesian algorithm-enabled multi-parameter optimization revealed that an adsorption pressure of 0.3 ~ 0.4 MPa increased helium recovery by 15%, while a 1.0 ~ 1.1 m adsorption bed height ensured product purity ≥ 99.999% with energy consumption maintained at 0.36 kWh/m3. Compared to the single-column system, the two-column configuration improved helium recovery by 5.06%. Notably, the novel PSA process reduces energy consumption by up to 60% compared to conventional methods, providing a data-driven and quantifiable design paradigm for lean helium purification and presenting an efficient and energy-saving technical solution for industrial-scale helium purification.