Integrated CO2 capture and conversion to methanol: A performance comparison of alcohol-amine solvents
Bibliographic record
Abstract
An integrated CO 2 capture and conversion (ICCC) process has been proposed to reduce the high energy requirement of solvent regeneration and gas compression in conventional carbon capture and utilization (CCU) process. This is achieved by capturing CO 2 in a solvent and then directly reacting the solvent captured CO 2 with H 2 in reactors to synthesize chemical products. This work investigates mixtures of tertiary amines, glycols, and alcohols to perform the integrated CO 2 capture and conversion to methanol in a hydrogenation slurry reactor using a Cu/ZnO/Al 2 O 3 catalyst. The CO 2 capture performance and methanol formation activity were first evaluated via gas-loaded CO 2 hydrogenation for pure solvents and amine mixtures, where hexanol-triethylamine mixture showed enhanced conversion performance. The integrated reaction was then studied by hydrogenation of liquid-captured CO 2 to establish the impact of alcohol-triethylamine solvents on methanol formation. The impacts of reaction temperature, H 2 pressure, and reaction time were investigated for hexanol-triethylamine mixtures. The recycling performance was also evaluated for the spent solvent and catalyst. The highest methanol activity of 5.6 mmol MeOH g cat −1 h −1 was achieved at 250 °C and 5 MPa with 48 % CO 2 conversion and 80 % selectivity to methanol using the 10:1 hexanol-triethylamine mixture, while a stable catalyst recycling performance was achieved by the 5:1 hexanol-triethylamine mixture at 170 °C and 5 MPa.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".