Metal Oxide-Based Catalysts for the Autothermal Reforming of Glycerol
Bibliographic record
Abstract
In this study hydrogen production from the autothermal reforming (ATR) of glycerol was investigated in a packed bed tubular reactor using nickel-based catalysts with theoretical composition of 5% Ni/Ce 0.5 Zr 0.33 M 0.16 O 2−δ, where M is the promoter element selected from Mg, Ca, Y, La, or Gd. The structural, textural, and physicochemical characteristics of the catalysts were investigated using various characterization techniques. The catalytic activity was evaluated in a temperature range from 450 to 700 °C; steam-to-glycerol (S/G) ratio of 6, 9, and 12; and oxygen-to-glycerol (O/G) ratio of 0.2, 0.5, and 0.8 at atmospheric pressure. Among all the catalyst formulations prepared in the current study, 5Ni/CeZrGd exhibited the best catalytic performance and stability compared to the other promoter elements. For 5Ni/CeZrGd catalysts, it was found that, until 600 °C, conversion increased rapidly with the increase in temperature to reach 82 mol % glycerol conversion and more than 70 mol % H 2 selectivity, which can be optimal operation conditions for industrial applications. Furthermore, the increase of O/G ratio showed similar trends. Although adding more S/G in the feed did not show any noticeable increase in glycerol conversion, it resulted in lower hydrogen concentration in the reformate product due to the dilution effect of steam.
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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".