Response surface modelling of <scp>CO<sub>2</sub></scp> capture by ammonia aqueous solution in a microchannel
Bibliographic record
Abstract
Abstract Post‐combustion CO2 capture is one strategy of greenhouse gases mitigation. Ammonia is a useful option as CO2 absorbent and an alternative to conventional amine‐based solutions. This study deals with CO2 capture by ammonia aqueous solution in a co‐current two‐phase flow by utilizing a T‐shaped microchannel. Three parameters of temperature, gas flow rate, and ammonia concentration were considered as the main parameters affecting the CO2 capture efficiency. A response surface methodology based on central composite design (CCD) was used to model the CO2 capture efficiency as output in terms of the aforementioned input variables. CCD suggested a quadratic model to fit the experimental data. The model validation was implemented by ANOVA. All statistic tools including correlation coefficient, P‐value, and F‐value of the model, and P‐value of lack‐of‐fit confirmed that the prediction model was significant. It was deduced from F‐values that the importance of the input variables followed the sequence of ammonia concentration > gas flow rate > temperature. Ammonia concentration was the most effective input variable because there was a direct correlation between ammonia concentration and the number of absorption sites in the liquid phase. Numerical optimization predicted the best output of 96.48% CO2 capture under the following optimum conditions: temperature of 20.00°C, gas flow rate of 110.59 mL/min, and ammonia concentration of 0.1382 mL/mL (13.82 vol%). The average CO2 capture of 95.42% obtained at the input conditions indicates the accuracy of the prediction model.
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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 | 0.001 |
| 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 | 0.000 |
| 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".