Solvent Regeneration of a CO<sub>2</sub>-Loaded BEA–AMP Bi-Blend Amine Solvent with the Aid of a Solid Brønsted Ce(SO<sub>4</sub>)<sub>2</sub>/ZrO<sub>2</sub> Superacid Catalyst
Bibliographic record
Abstract
The objective of the current research is to investigate the addition of a solid proton donor catalyst such as HZSM-5 and Ce(SO 4 ) 2 /ZrO 2 to the amine solvent regeneration process at the operation temperature of 85 °C. The HZSM-5 zeolite with Si/Al ratios of 5 to 20 was synthesized by hydrothermal method. The solid superacid catalyst, Ce(SO 4 ) 2 /ZrO 2, was prepared by simply doping ZrO 2 with Ce and modifying with sulfate simultaneously. Various techniques such as X-ray diffraction, energy-dispersive X-ray spectrometry, Brunauer―Emmett―Teller, NH 3 -temperature-programmed desorption, and pyridine Fourier transform infrared spectroscopy were used to characterize the solid acid catalysts. The performance of the catalysts was evaluated in terms of their CO 2 desorption rate, CO 2 cyclic capacity, and heat duty. The experimental results indicated that the addition of the catalyst to the solvent regeneration process showed simultaneous increase in CO 2 desorption rate and cyclic capacity as well as reduction of heat duty. The heat duty of the Ce(SO 4 ) 2 /ZrO 2 catalyst was 62 kJ/mol CO 2, which required 56% less energy for regeneration than without catalyst (110 kJ/mol CO 2 ). The solid catalyst could help CO 2 desorption by providing free protons. The Ce(SO 4 ) 2 /ZrO 2 catalyst exhibited a much better performance than the HZSM-5 catalysts, as it possessed a strong acid site and a large amount of Brønsted acid sites as well as a large pore size, which effectively facilitated CO 2 desorption.
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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 | 0.000 |
| 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 | 0.000 |
| 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".