Membrane separation system for coal‐fired flue gas reclamation: Process planning and initial design
Bibliographic record
Abstract
Abstract Membrane separation is highly expected for enriching CO2 from flue gases, but the inadequacy in separation efficiency and production capacity should not be overlooked. Process planning and initial design with multi‐product and energy recovery approach is attempted in this work to ameliorate this limitation. Three retrofitting options are considered: the novel constraint mode with the local permeate CO2 concentration that is always higher than the feed stream concentration is proposed for CO2 enrichment to increase the output of the CO2‐enriched product (with a general mode, the local permeate stream that is more diluted than the feed stream mixes in the permeate bulk); the turbine unit is used to take back the static energy in membrane residue; and the multi‐product option with N2 purification would have the advantage that N2 has been enriched greatly after CO2 separation. According to the comprehensive comparison conducted in this study, the retrofitted process involving both the novel constraint mode and the multi‐product approach is the most effective for saving energy and enhancing production capacity. Crude CO2 (50 vol%) and N2 (99 vol%) would be generated simultaneously. In addition, the positive performance has been demonstrated by process simulation and economical analysis with both a normal polysulphone membrane and a novel Polaris membrane. On the whole, membrane separation system retrofitting with multi‐product and energy recovery approach together should be an effectual approach to promote carbon capture, storage, and utilization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".