Practically Achievable Process Performance Limits for Pressure-Vacuum Swing Adsorption-Based Postcombustion CO <sub>2</sub> Capture
Bibliographic record
Abstract
Practically achievable limits for pressure-vacuum swing adsorption (PVSA)-based postcombustion carbon capture are evaluated. The adsorption isotherms of CO 2 and N 2 are described by competitive Langmuir isotherms. Two low-energy process cycles are considered and a machine learning surrogate model is trained with inputs from an experimentally validated, detailed PVSA model. Several case studies are considered to evaluate two critical performance indicators, namely, minimum energy and maximum productivity. For each case study, the genetic algorithm optimizer that is coupled to the machine learning surrogate model searches tens of thousands of combinations of isotherms and process operating conditions. The framework pairs the optimum materials properties with the optimum operating conditions, hence providing the limits of achievable performance. The results indicate that pressures < 0.2 bar may be required to achieve process constraints for feeds with low CO 2 compositions (<0.15 mole fraction), indicating that PVSA may not be favorable. At higher CO 2 feed compositions, PVSA can be attractive and can be operated at practically achievable vacuum levels. Further, the gap between the energy consumption of available adsorbents and the achievable limits with the best hypothetical best adsorbent varies between 20 and 2.5% as the CO 2 feed composition changes between 0.05 and 0.4. This indicates a limited potential for the development of new adsorbents of PVSA-based CO 2 capture. Future work for PVSA should focus on gas streams with high CO 2 compositions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".