Evaluating CO<sub>2</sub> Capture Performance of Trisolvent MEA–BEA–AMP with Heterogeneous Catalysts in a Novel Bench-Scale Pilot Plant
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide To reduce the huge energy cost of CO 2 capture technology applicable in industry, the CO 2 absorption–desorption performance was conducted in a novel bench-scale pilot plant with hot water as a heat source. The trisolvent MEA(monoethanol amine)–BEA(butylethanol amine)–AMP(2-amino-2-methyl-1-propanol) was prepared at a specific concentration to analyze the CO 2 capture performance and compared with 5 M MEA as the benchmark. Meanwhile, several solid acid catalysts, blended H-ZSM-5/γ-Al 2 O 3 (1/2), or HND-8, were packed in the desorber, and the solid base catalyst, CaCO 3 or CaMg(CO 3 ) 2, was packed in the absorber with random packing. The CO 2 absorption efficiency (AE), cyclic capacity (CC), and heat duty (HD) were tested onto MEA–BEA–AMP and MEA under various operating conditions. Experimental results indicated that the performance of 4.3 mol/L MEA–BEA–AMP was significantly better than 5 M MEA under both catalytic and noncatalytic operation. The most energy efficient combination of this study was discovered as 0.3 + 2 + 2 mol/L MEA–BEA–AMP, with 50 g (CaCO 3 /CaMg(CO 3 ) 2 ) in the absorber and 150 g H-ZSM-5/γ-Al 2 O 3 (1/2) in the desorber. The heat duty reached as low as 2.4 GJ/tCO 2 at a F G of 7.0 L/min and a F L of 70 mL/min. These results were highly applicable in an industrial amine scrubbing pilot plant for CO 2 capture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".