Removal of nitric oxide in a microporous tube‐in‐tube microchannel reactor by ferrous chelate solution
Bibliographic record
Abstract
Abstract NO gas emission has harmfully impacted human health and the environment. Absorption of NO gas into an aqueous solution is considered to be a promising approach for its removal. Fe II (EDTA) solution has been demonstrated as an efficient option in the denitrification process. However, its absorption rate is strongly affected by mass transfer limitation of NO into Fe II (EDTA) solution in traditional reactors. In this paper, the removal process of NO with Fe II (EDTA) solution was studied in a microporous tube‐in‐tube microchannel reactor (MTMCR). The effects of design and operating parameters such as micropore size, annular channel width, liquid flow rate, gas flow rate, gas‐liquid ratio, pH and concentration of absorbent, and absorption temperature on overall volumetric mass transfer coefficient ( K L a ) and NO removal efficiency were explored. The results indicated that the MTMCR exhibited obvious advantages owing to continuous operation mode and higher NO removal efficiency of over 90 %, as compared to traditional reactors. Both K L a and NO removal efficiency increased with increases of absorbent concentration and liquid flow rate, as well as decreases of absorption temperature, micropore size, and annular channel width. In addition, K L a increased while NO removal efficiency decreased with increasing gas‐liquid ratio and gas flow rate. The obtained results imply a great potential of the MTMCR in the removal of NO from post‐combustion flue gas.
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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 | 0.000 |
| 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".