New Reactive Extraction Based Reclaiming Technique for Amines Used in Carbon Dioxide Capture Process from Industrial Flue Gases
Bibliographic record
Abstract
A new reclaiming technique based on reactive extraction for removal of heat stable salts (HSS) from monoethanolamine (MEA) used in carbon dioxide (CO 2 ) capture process has been developed. The extraction process was based on the use of tri- n -octylamine (TOA), Aliquat 336, OH – modified Aliquat 336, a two-step extraction (modified Aliquat followed by TOA), and mixed extractant (modified Aliquat and TOA) in 1-octanol diluent. The best parameters were 69% OH – modified Aliquat, two-step extraction, and mixed extractant (modified Aliquat and TOA) and under optimum extraction conditions were able to improve the extraction efficiency of the original Aliquat and TOA to over 90%. The two-step extraction and mixed extractant were also capable of managing Cl – contamination in MEA solution. Extraction was found to be independent of temperature whereas efficiency reduced with increase of CO 2 loading. Therefore, it is recommended to apply this extraction technique to the lean MEA stream after the rich/lean heat exchanger either with or without cooling. Regeneration of used extractant (OH Aliquat) was implemented and optimized with the use of 4 kmol/m 3 NaOH. In addition to HSS removal, the new extraction technique was also able to remove major nonionic degradation products also by up to 90%.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".