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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".