Kinetic Model of Catalytic Steam Gasification of 2-Methoxy-4-methylphenol Using 5% Ni–0.25% Ru/γAl2O3 in a CREC-Riser Simulator
Bibliographic record
Abstract
Hydrogen is an energy vector with a great potential due its ample range of applications and clean combustion cycle. Hydrogen can be produced through biomass steam gasification, with novel catalysts being of significant value to implement this process. With this goal in mind, in the present study, 5 wt % Ni/γAl2O3 promoted with 0.25 wt % Ru was synthesized and characterized. It is assumed that ruthenium facilitates hydrogen transfer to nickel oxide sites, promoting a hydrogen spillover effect, with the H2 adsorbed on Ru being transported to Ni sites. To describe chemical changes, the present study considers a kinetic model involving Langmuir–Hinshelwood-based rate equations, as a sum of independent reactions, with this being applied to the steam gasification of 2-methoxy-4-methylphenol (2M4MP). This tar biomass surrogate was studied in a fluidized CREC (Chemical Reactor Engineering Centre) Riser Simulator reactor, at different reaction times (5, 20 and 30 s.) and temperatures (550 °C, 600 °C and 650 °C). The proposed kinetics model was fitted to the experimentally observed H2, CO2, CO, CH4 and H2O concentrations, with the estimated pre-exponential factors and activation energies being in accordance with the reported literature data. It is anticipated that the postulated model could be of significant value for the modeling of other biomass conversion processes for hydrogen production using other supported catalysts.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".