Mechanistic Kinetic Modeling of Oxidative Steam Reforming of Bioethanol for Hydrogen Production over Rh–Ni/CeO<sub>2</sub>–ZrO<sub>2</sub> Catalyst
Bibliographic record
Abstract
A kinetic study was carried out over Rh–Ni/CeO 2 –ZrO 2 catalyst for the oxidative steam reforming of ethanol (OSRE). A Langmuir–Hinshelwood approach based on proposed surface reaction mechanisms was used to develop the kinetic models for the OSRE process. Oxidative steam reforming (OSRE), ethanol decomposition (ED), and water–gas shift (WGS) reactions were considered as the main reaction pathway to represent the overall OSRE process. Kinetic data were collected in a fixed bed reactor under the kinetic-control regime at three different temperatures. The kinetic parameters were estimated using a nonlinear regression method. The kinetic model was developed by considering dehydrogenation of adsorbed ethoxy species, decomposition of formate species, and decomposition of acetaldehyde as the rate-determining step for OSRE, WGS, and ED reactions, respectively. The developed model fitted well with the experimental observations at all studied temperatures and contact time. The activation energy for OSRE, WGS, and ED reactions obtained was 56.0, 46.1, and 34.8 kJ/mol, respectively. The results revealed that the proposed Langmuir–Hinshelwood mechanistic kinetic model (model LH-II) is suitable for the OSRE process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".