Pilot Plant Study of Novel AMP–Based Amine Solvent Blend for CO2 Capture: Rich Amine Loading, Absorption Rate, Absorber Overall Volumetric Mass Transfer Coefficient and Amine Cost
Bibliographic record
Abstract
This pilot plant study covered the CO2 absorption potential of a novel bi-solvent blend containing AMP and 1,5–diamino–2–methylpentane (DA2MP). The CO2 concentration was kept at 15.1 vol.% to represent the flue gas from a coal-fired power plant. The simulated flue gas flow rate and the amine volumetric flow rate were 14 SLPM and 50 ml/min respectively. Comparative CO2 absorption analysis was conducted for the AMP-DA2MP blend and MEA (5 kmol/m3 or 30 wt.%). The concentration of AMP is 2 kmol/m3 while that of DA2MP was varied from 2 kmol/m3 to 3 kmol/m3 making the total concentration 4 kmol/m3 and 5 kmol/m3. The comparative analysis is based on rich amine loading, CO2 absorption rate, absorber overall mass transfer coefficient, and initial amine solution cost. The parametric effect of lean amine loading on the above-mentioned parameters was also investigated. Pilot plant results showed that the rich amine loading, CO2 absorption rate and absorber mass transfer coefficient of the AMP-DA2MP blend is higher than the single solvent MEA. However, the initial amine solution cost of MEA is discovered to be lower than the AMP-DA2MP blend. For all the amine solutions studied, increase in lean amine loading resulted in an increase in rich amine loading, decrease in absorber mass transfer coefficient, decrease in CO2 absorption rate and increase in initial amine solution cost. Importantly, higher lean amine loadings of the amine blend have higher CO2 absorption rate and higher absorber mass transfer coefficient compared to lower lean loadings of MEA. Overall, it is an early indication that the AMP-DA2MP blend will offer a cost-effective CO2 capture compared to the benchmark MEA solvent.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".