Preparation and stability of zinc‐based mesoporous sorbent modified with aluminium for high temperature coal gas desulphurization
Bibliographic record
Abstract
Abstract Zinc‐based sorbents with ordered mesoporous structure were modified with aluminium and the key factors in the preparation were optimized based on the results of desulphurization tests performed in a fixed‐bed reactor with simulated coal gas. It was shown that the sorbents for hydrogen sulphide removal reached an optimum sulphur capacity of 9.14% when prepared under conditions of 10.0 crystalline pH, 30:1 Si:Al molar ratio, 0.32:1 Zn:Si molar ratio, and 1:3 Zn:TAA molar ratio. The sorbent without aluminium was synthesized synchronously as a comparison sample to investigate the effect of aluminium addition on the desulphurization properties. The surface acidity of the sorbents is enhanced by the addition of aluminium, and the sulphur capacity of the aluminium‐doped sorbent is consequently lower compared to that of aluminium‐free sorbent. Nevertheless, the aluminium‐doped sorbent shows a significant advantage in stability of performance over multiple desulphurization–regeneration cycles and reaches an 81% retention rate of sulphur capacity after five desulphurization, while the aluminium‐free sorbent is only 51% in contrast. The characterization results manifest that aluminium enters the carrier skeleton and increases the wall thickness, which alleviates the collapse of the carrier pore channels and the agglomeration of the active components during the desulphurization process. Stable pore structures and highly dispersed active components facilitate the mass transfer in the reaction process after multiple desulphurization. As a result, the aluminium‐doped sorbent exhibits better performance stability in high temperature coal gas desulphurization.
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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 | 0.000 |
| 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".