Cycle Development and Process Optimization of High-Purity Oxygen Production Using Silver-Exchanged Titanosilicates
Bibliographic record
Abstract
Producing oxygen with purity higher than 95.0% from atmospheric air (78.0% N 2, 21.0% O 2, and 1.0% Ar) is challenging because of the similar physical properties of oxygen and argon. Silver-exchanged titanosilicates have shown the potential to separate these gases based on their thermodynamic affinities. In this study, various vacuum swing adsorption (VSA) cycle configurations including the simple Skarstrom cycle and more complicated 6-step VSA cycles were simulated using mathematical models to maximize O 2 purity and recovery. The simulations were verified by conducting simple 3-step and Skarstrom VSA cycle experiments. A mixture of 95.0%/5.0% O 2 /Ar feed was considered in the simulations, and a rigorous multiobjective optimization was conducted to maximize O 2 purity and recovery. The simulations predicted 27.3% recovery for a product with 99.5% purity for a 6-step cycle with pressure equalization and light product pressurization steps. The recovery for the same level of purity was improved significantly to 91.7% by implementing a heavy product pressurization step. The effect of bed length on O 2 purity and recovery and the comparison of VSA with pressure swing adsorption and pressure–vacuum swing adsorption for high-purity O 2 production were also investigated. Rigorous multiobjective optimizations were conducted to maximize oxygen productivity and minimize energy consumption of the VSA cycles, while meeting different purity constraints, and significant improvement in the performance indicators was obtained through process optimization.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".