Mechanistic Kinetic Modelling Framework for the Conversion of Waste Crude Glycerol to Value-Added Hydrogen-Rich Gas
Bibliographic record
Abstract
The kinetics for crude glycerol autothermal reforming was studied over S/C ratio of 2.6 and O2/C ratio of 0.125 using 5% Ni/CeZrCa catalyst. Both power law and mechanistic kinetic models were studied. The overall power law model for crude glycerol autothermal reforming was investigated with a pre-exponential factor of 4.3 × 1010 mol/gcat·min and activation energy of 8.78 × 104 J/mol. The reaction orders with respect to crude glycerol, water and oxygen are 1.04, 0.54 and 1.78 respectively. The power law model presented an absolute average deviation of 5.84%, which showed a good correlation between the predicted and experimental rate. Mechanistic models were developed for crude glycerol autothermal reforming. For steam reforming, the Eley–Rideal approach best described the reaction rate with the surface reaction being the rate-determining step (AAD < 10%). The kinetics of the total oxidation reaction was best described by the power law model with an AAD of less than 1%, whereas for the TOR process, the molecular adsorption of crude glycerol with an AAD of 14.6% via Langmuir Hinshelwood Hougen-Watson approach was best. CO2 methanation resulted in an AAD of 5.8% for the adsorption of carbon dioxide (CO2) by the Eley–Rideal mechanism.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".