Optimization of pressure-vacuum swing adsorption processes for nitrogen rejection from natural gas streams using a nitrogen selective metal organic framework
Bibliographic record
Abstract
A vanadium(II/III) metal-organic organic framework (MOF), V2Cl2.8(btdd), that is selective to CH4 over N2 was recently discovered. Process optimizations were performed to determine the performance of this MOF to reach the pipeline transport purity of 96 mol% CH4. Two cycles were considered: the basic 3-step cycle and the Skarstrom cycle. First, the 3-step cycle was considered with a wide range of operating conditions. Three inlet compositions (55/45, 80/20 and 92/8 mol% CH4/N}), three process temperatures (30, 40 and 50C) and a range of adsorption pressures (100 to 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 3-step cycle was unable to meet the 96 mol% CH4 purity requirement in most cases studied. However, the Skarstrom cycle was able to meet the 96 mol% CH4 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 50C and an adsorption pressure of 100 kPa. For the Skarstrom cycle, at a feed temperature of 50C, 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 CH4 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".