Dry reforming of methane at high temperature and elevated pressure over nickel spinellized powder catalyst and pellets prepared from a metallurgical residue
Bibliographic record
Abstract
Abstract The coke deposition on catalysts is a significant problem in the dry reforming of methane at elevated pressures. Understanding and controlling the mechanisms of such deposition is essential in developing a techno‐economically viable industrial application for the production of synthesis gas and/or hydrogen. The patent‐pending nickel‐supported upgraded slag oxide (Ni‐UGSO) catalysts, in powder form, have demonstrated excellent performance and achieved equilibrium in dry reforming, steam reforming and mixed methane reforming in a gram‐scale laboratory packed bed reactor under barometric pressure. In this extended study, Ni‐UGSO pellets were prepared using the wet impregnation method. The pelletized form of said catalyst was studied under elevated pressure to imitate the industrial operating conditions in a kilogram‐scale laboratory packed bed reactor. The characterization of the fresh and used catalytic formulation produced data allowing the investigation of the physicochemical properties of catalysts and the effects of metal dispersion, reaction pressure and crystallite size, as well as the role of side reactions on the nature of the coke. The metal support nature favored the interaction between the Ni metal and spinels (UGSO), and the presence of the clay binder (kaolinite, quartz) improved the pellet morphology, provided higher Ni dispersion, maintained the crystallite size, reduced the coke formation and achieved similar or higher performance with respect to Ni‐UGSO powder despite having 85% less surface area. The Ni‐UGSO pellet showed negligible coke deposits from 1 to 6.5 atm and operated successfully for 24 h at 5.5 atm, 800°C and gas hourly space velocity 810 L/(h kg cat). This study provides new insight into the design of a more efficient and robust catalyst for methane dry reforming at elevated pressures, which is critical for potential future transfer at the industrial level.
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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) | 0.001 | 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".