Corrosion Performance of Austenitic Stainless Steels under Hydrodeoxygenation Upgrading of Pyrolysis Oils Using Supercritical Ethanol
Bibliographic record
Abstract
Abstract Crude pyrolysis bio-oils are recognized as a potential source to replace conventional fuels and chemicals. However, their high water content, viscosity and acidity significantly hinder industrial applications. Hydrodeoxygenation Upgrading (HDO) of pyrolysis bio-oil, can remarkably improve their quality and advanced the application of being as an alternative fuel or chemical. During the upgrading, the high contents of water and acids in of the crude bio-oil may introduce unwanted corrosion damage to the processing equipment. This paper investigated the corrosion performance of two candidate constructional steels (UNS S31603 and UNS S30400) under the HDO processes using supercritical ethanol solvent, NiMoW/Al2O3 catalyst and different hydrogen resources (hydrogen gas or formic acid) at 325 °C. The introduction of H2 gas or formic acid application of formic acid could effectively improve upgrading efficiency. The application of formic acid led to a less aggressive upgrading environment to the steels. UNS S31603 exhibited a better corrosion resistance under the catalytic HDO process compared to UNS S30400.
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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) | 0.001 | 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".