Optimization of pressure‐vacuum swing adsorption processes for nitrogen rejection from natural gas streams using a nitrogen selective metal organic framework
Bibliographic record
Abstract
Abstract A vanadium(II/III) metal–organic framework (MOF), V 2 Cl 2.8 (btdd), that is selective to N 2 over CH 4 was recently discovered. Process optimizations were performed to determine the performance of this MOF to reach the pipeline transport purity of 96 mol% CH 4 . Two cycles were considered: the basic three‐step cycle and the Skarstrom cycle. First, the three‐step cycle was considered with a wide range of operating conditions. Three inlet compositions (55/45, 80/20, and 92/8 mol% CH 4 /N 2 ), three process temperatures (30, 40, and 50°C) and a range of adsorption pressures (100–500 kPa) were considered. A detailed process model in tandem with machine learning‐aided optimization was employed to determine the optimal set of operating conditions. The three‐step cycle was unable to meet the 96 mol% CH 4 purity requirement in most cases studied. However, the Skarstrom cycle was able to meet the 96 mol% CH 4 purity requirement in all cases studied. The maximum recovery, at a purity of 96 mol%, was at 84.2% for the Skarstrom cycle with a methane feed composition of 80 mol% at 50°C and an adsorption pressure of 100 kPa. For the Skarstrom cycle, at a feed temperature of 50°C, an adsorption pressure of 100 kPa and a feed methane composition of 92 mol%, the productivity could be as high as 21.18 tonnes per day CH 4 m −3 at a recovery of 73%. The achievable recovery‐productivity values were comparable to a carbon molecular sieve process reported in the literature at similar operating conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 source (direct Gemma or distilled Codex), 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".