Enhancing CO2 hydrogenation to methanol in fixed and fluidized bed reactors by selective in-situ adsorption of water
Bibliographic record
Abstract
Methanol synthesis offers a significant pathway for carbon dioxide valorization and hydrogen storage. However, carbon dioxide hydrogenation to methanol is thermodynamically limited. To couple the process with biogenic carbon dioxide supply from organic wastes and green hydrogen, a way to shift the equilibrium by other means than the pressure must be found. This work investigates the underlying aspects of sorption-enhanced methanol synthesis by selective steam removal in fixed and bubbling fluidized bed configurations. Zeolite 3 A is chosen as a suitable sorbent based on its selectivity towards water and retention of sorption capacity between 220 and 280 °C. A dynamic model for simulation of the fixed-bed sorption-enhanced methanol synthesis shows reactor performance at the targeted application pressure of 20–30 bar. Enhanced product yields beyond thermodynamic equilibrium limits are obtained in a lab-scale reactor at 3 bar and 220–250 °C via over-stochiometric water adsorption. In both reactor configurations, low-pressure enhancement primarily promotes carbon monoxide yield. Predicted methanol yields at higher pressures reach similar values, which suggests implementing a two-stage process. The additional production relative to the total output expected under full equilibrium limitation results in a 14–23 % integrated enhancement for methanol and 17–20 % for carbon monoxide, depending on temperature and reactor configuration. During the sorption-enhancement peak, the maximum achievable yields reach 130–175 % and 160–185 %, respectively. The transient nature of sorption enhancement is highlighted, suggesting a fluidized reactor design for continuous regeneration in a separate vessel, promoted by a sharper water breakthrough than in a fixed bed, a more compact reactor volume, and improved temperature distribution.
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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) | 0.000 | 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 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".