Equilibrium and dynamic adsorption characteristics of zeolite 5A, LiX, 13X and MOF UTSA-16 adsorbents for hydrogen purification
Bibliographic record
Abstract
Zeolite materials are widely used as adsorbents in pressure swing adsorption (PSA) technology for hydrogen purification, particularly excelling in removing weakly adsorbed gases such as N 2 and CO. The selection of zeolite materials is crucial for enhancing the performance of hydrogen purification. This study investigates the adsorption capacity , selectivity and working capacity of three widely used zeolite adsorbents (5A, LiX and 13X) and a zeolite-like material (UTSA-16) for hydrogen purification from steam methane reforming off-gas (SMROG), composed of H 2 /CO 2 /CH 4 /CO = 73/16/8/3 mol%. The results indicate that, based on adsorption isotherms , Zeolite LiX exhibits the strongest adsorption capacity for CH 4 and CO. From the perspective of selectivity, LiX demonstrates the highest S (CO2+CH4+CO)/H2 value, making it preliminarily identified as an ideal adsorbent for hydrogen purification. In terms of working capacity, UTSA-16 shows the highest working capacity for CO 2 and CH 4 , making it more suitable for scenarios involving the removal of CO 2 in layered adsorption bed designs, while 13X exhibits the highest working capacity for CO. To further evaluate the dynamic performance of these adsorbents for hydrogen purification, the adsorption, heat and mass transfer model for multi-component gas mixtures was established by Aspen Adsorption software. The simulation results align well with experimental data. In the analysis of the dynamic adsorption characteristics of typical SMROG mixtures, a comparison of the dimensionless breakthrough times ( τ break ) for CO and CH 4 on various adsorbents reveals the following order: LiX >13X > 5A > UTSA-16. Based on this dynamic performance indicator, Zeolite LiX is regarded as the material with the best overall performance among the four adsorbents. By analyzing the equilibrium and dynamic adsorption characteristics in terms of selectivity, working capacity and dynamic breakthrough curve , this study not only elucidates the adsorption behavior of different adsorbents in multi-component gas separations but also provides theoretical insights and practical guidance for optimizing adsorbent selection in PSA systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".