ETHANOL REFORMING FOR HYDROGEN PRODUCTION IN HIGH PRESSURE CONDITION: EFFECT OF DIFFERENT SUPPORT ON COBALT BASED CATALYST
Bibliographic record
Abstract
In this study the steam reforming of Ethanol under the Cobalt catalyst with different support was studied, the yield of Hydrogen with respect to different support was measured. Catalysts were prepared on laboratory scale and its properties such as, (TPD), (TPR), XRD, surface area and pore volume were measured. The analysis of liquid and gas products were done by G.C. and found to be varying with different support. Under the supercritical condition of water the support can affect the catalyst physically as well as chemically. The degree of catalyst reduction can effect the Hydrogen production. The C2H4 production is favoured by Cobalt oxide. The comparison of XRD of YSZ and Co2O3 over YSZ catalyst, suggests that the Cobalt (III) oxide in the presence of hydrogen reduced to Co II and then to metallic cobalt. The degree of Cobalt reduction affects directly the amount of products. TPR of catalysts show that Co2O3/YSZ and Co2O3/ZrO2 reduce by H2 below 500 o C in favour of more hydrogen production. The catalyst tendencies for unfavorable product CO are very low. The production of CO2, CH4 and especially Hydrogen is increasing with Co2O3/YSZ and Co2O3/ZrO2. In the liquid phase the amount of Acetal and Acetone are more than Ethyl ether. The results of this work suggest that Co/YSZ shows high conversion of Ethanol to Hydrogen as well as a good resistance toward deactivation under high pressure. Keyword: Ethanol, Steam reforming, hydrogen production, Cobalt catalyst, Catalyst support, supercritical water.
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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".