Biodiesel reforming with a NiAl<sub>2</sub>O<sub>4</sub>/Al<sub>2</sub>O<sub>3</sub>-YSZ catalyst for the production of renewable SOFC fuel
Bibliographic record
Abstract
Biodiesel's contribution as a renewable energy carrier is increasing continuously.Fuel cell market penetration, although slow, is now an irreversible reality.The combination of solid oxide fuel cells (SOFC) with biodiesel offers considerable advantages because it entails both high energy conversion efficiency and nearzero atmospheric carbon emissions.This work is aimed at proving the efficiency of a newly-developed (patent pending), Al 2 O 3 /YSZ-supported NiAl 2 O 4 spinel catalyst to steam reform biodiesel.Reforming converts biodiesel into a gaseous mixture, mainly composed of H 2 and CO, used directly as SOFC fuel.The work is performed in a test rig comprising a lab-scale, fixed-bed isothermal reactor and a product-conditioning train.The biodiesel/water mixtures are emulsified prior to their spray injection in the reactor preheating zone, where they are instantaneously vaporized and rapidly brought to the desired reaction temperature to avoid thermal cracking.Reforming takes place at gas hourly space velocities equal to or higher than those in industrial reforming units.The products are analysed by at-line gas chromatography.The results show that biodiesel conversion is complete at steady state.Thermodynamic calculations reveal that the fast reforming reaction reaches chemical equilibrium.The catalyst's performance is very efficient and prevents carbon formation and deactivation.
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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) | 0.002 | 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".