The Pyrolysis of Glycerol Using Microwave for the Production of Hydrogen
Bibliographic record
Abstract
The purpose of this research was to study pyrolysis of glycerol to produce hydrogen using microwave. The useof microwave aimed to produce high temperatures, because pyrolysis require high temperature.The effect of kindof catalyst and microwave power were studied. The catalyst was activated carbon and Ni/HZSM-5.The catalystof activated carbon was ready to use, whereas Ni/HZSM-5 catalyst was obtained by ion exchange fromNa-ZSM-5 with NH4Cl and then HZSM-5 was impregnated with metal solution of Ni (NO3)2.6 H2O.Experiments were conducted by mixing catalyst in the reactor together with glycerol solution of 10% (weightpercent) as much as 100 ml. Reactor was made from pyrex and mounted on microwave equipped with athermocouple. And then, reactor was heated on power of 400-700 Watt during thirty minutes. The reactionproduced gases and liquid to be analyzed by chromatography gas.The conclusion stated that microwave couldpyrolysis glycerol into hydrogen. By product of this reaction were methanol, allyl alcohol, acrolein andunidentified products. The difference of catalyst produced different product as well. The pyrolysis of glycerolusing activated carbon produced conversion of 60 %, while using catalysts Ni/HZSM-5 obtained conversion of87 %. The reaction produced hydrogen gases was relatively small for both of catalysts that is minimum of 0,59%and maximum of 0,88%.
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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".