Thermodynamic optimization of syngas production via auto-thermal oxidative steam reforming of bio-oil
Bibliographic record
Abstract
This contribution presented a detailed thermodynamic analysis and numerical optimization of auto-thermal oxidative steam reforming (ATR/OSR) of a typical bio-oil under various operating conditions. The objective was to achieve an H 2 /CO ratio of 2.15 while maximizing the generation of syngas and avoiding carbon formation. A thermodynamic equilibrium model coupled with response surface methodology (RSM) was applied to assess the effect of various operating parameters, including temperature, steam-to-bio-oil molar ratio, and equivalence ratio (ER), on the product distribution and syngas quality. The optimal condition was identified under an ER of 0.3, a steam-to-bio-oil molar ratio of 1.01, and an operating temperature of 924 K, yielding carbon-free syngas with H 2 /CO = 2.15, and a total syngas yield of 1.33 mol/mol C . A comparison of the optimum points with prior works validates the consistency of the thermodynamic and experimental data and highlights the feasibility of the proposed concept. This work therefore demonstrates a practical, carbon-negative, and energy-neutral route to generate FTS-ready syngas directly from bio-oil under moderate conditions. • A thermodynamic equilibrium model of real bio-oil was developed for auto-thermal oxidative steam reforming (ATR/OSR). • Multi-objective optimization via RSM determined the optimum at ER = 0.30, S/C = 1.01, and T = 924 K. • Maximum syngas yield of 1.33 mol.mol C −1 and a syngas ratio of H 2 /CO = 2.15 was attained under optimum conditions. • The process operated under thermoneutral conditions (ΔH ≈ 0 kJ mol −1 ) with zero carbon formation. • The study proposes a sustainable route to generate FTS-ready syngas from bio-oil without extra units.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".