Chemical absorption of <scp>CO<sub>2</sub></scp> in alkaline solutions using an intensified reactor
Bibliographic record
Abstract
Abstract Capturing carbon dioxide (CO2) emissions from point sources is critical for a sustainable chemical industry. Several techniques have already been developed and the race is ongoing to meet more stringent limitations at lower operating costs. This study presents the capability of a novel, flexible reactor to achieve high absorption rates at very low power consumption. CO2 absorption in an aqueous sodium hydroxide (NaOH) solution was used as a benchmark. An air–CO2 stream made of 30% v/v CO2 was injected co‐currently with the aqueous alkaline solution in a tubular reactor equipped with woven mesh mixers. The removal efficiencies were measured along the length of the reactor, which operated at total mean flow velocities ranging between 1–2 m/s and gas phase holdups between 10%–30%. Four different mixer geometries were also tested, and the results were analyzed based on the operating conditions and reactor design configurations. While these studies can be further optimized and potentially applied in carbon capture operations, it was found that more than 96.5% of the CO2 could be removed using different combinations of mixer geometry and operating conditions. This high removal efficiency was reached within a residence time of 400 ms at a low cost of 23.65 .
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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 | 0.000 |
| 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 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".