Bibliographic record
Abstract
Abstract An Air Products-led team is developing a new air separation technology - Ion Transport Membrane Oxygen - based on ceramic membranes that selectively transport oxygen ions when operated at high temperature. Under the influence of an oxygen partial-pressure driving force, the ITM Oxygen process achieves a high-purity, high-flux separation of oxygen from air. By integrating the energy-rich, vitiated, non-permeate stream with a gas turbine system, the overall process co-produces high-purity oxygen, power, and steam if desired. As a result, the technology is ideally suited for advanced energy conversion processes such as IGCC that require oxygen and use heavy carbonaceous feedstocks (resid oils, bitumens, coke, coal), as well as for traditional industrial applications for oxygen and distributed power. During Phases I and II of a three-phase development program with the U.S. Department of Energy, the ITM Oxygen team established the feasibility of the ceramic membrane approach and designed and built commercial-scale membrane modules. A prototype facility that will produce 5 tons-per-day of oxygen will start up this year. Larger-scale test rigs are planned, with commercialization projected in the latter part of the decade. This paper will present an overview and status of the ITM Oxygen development effort, and the integration into gasification and clean energy processes. Process integration options will be discussed and contrasted. The economic benefits compared to the best commercial air separation alternative will be highlighted.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".