Advanced hydrogen and CO2 capture technology for sour syngas
Bibliographic record
Abstract
A key challenge for future clean power or hydrogen projects via gasification is the need to reduce the overall cost while achieving significant levels of CO2 capture. The current state of the art technology for capturing CO2 from sour syngas uses a physical solvent absorption process (acid gas removal–AGR) such as Selexol™ or Rectisol® to selectively separate H2S and CO2 from the H2. These two processes are expensive and require significant utility consumption during operation, which only escalates with increasing levels of CO2 capture. Importantly, Air Products has developed an alternative option that can achieve a higher level of CO2 capture than the conventional technologies at significantly lower capital and operating costs. Overall, the system is expected to reduce the cost of CO2 capture by over 25%. Air Products developed this novel technology by leveraging years of experience in the design and operation of H2 pressure swing adsorption (PSA) systems in its numerous steam methane reformers. Commercial PSAs typically operate on clean syngas and thus need an upstream AGR unit to operate in a gasification process. Air Products recognized that a H2 PSA technology adapted to handle sour feedgas (Sour PSA) would enable a new and enhanced improvement to a gasification system. The complete Air Products CO2 Capture technology (CCT) for sour syngas consists of a Sour PSA unit followed by a low-BTU sour oxycombustion unit and finally a CO2 purification / compression system.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".