Corrosion Performance of Candidate Fe-Cr-Ni Alloys with Different Cr and Ni Contents under Biomass Hydrothermal Conversion Process
Bibliographic record
Abstract
Abstract Biomass hydrothermal liquefaction (HTL) is usually operated in harsh environments due to the presence of hot pressurized water reaction medium, alkaline catalysts, inorganic and organic corrosive constituents released during the conversion. Corrosion knowledge gaps exist on the selection of suitable alloys of construction. Previous studies in high temperature aqueous solutions have indicated that the contents of alloying elements (such as Cr and Mo) are key factors influencing corrosion protection of Fe-Ni-Cr alloys. Increasing the contents of Cr and/or Ni in constructional alloys may alter the corrosion mode and reduce the extent, leading to an acceptable long-term performance of the refining reactor. This study investigates the corrosion performance of three candidate alloys with varying Cr and Ni contents, including P91 UNS K91560 (Fe-9Cr), SS304 UNS S30400 (Fe-18Cr-8Ni), and Alloy 33 UNS R20033 (Fe33Cr-31Ni) in a static autoclave containing a simulated aqueous (inorganic) phase of a biomass conversion product mixture (800 ppm KCl + 1M K2CO3) at 310°C for 10 days. Post exposure examinations on corroded alloys show that the surface scales grown on the lower alloyed steels (P91 and SS304) consists of an (Fe,Cr)3O4 spinel layer. Conversely, the oxide scale on the highly alloyed Alloy 33 consists of a multi-layered outer Ni-rich oxide, middle Ni-Cr-Fe spinel, and inner Cr-rich oxide. The thicknesses of the oxide layers formed on P91, SS304, and Alloy 33 were approximately 7 μm, 1.1 μm, and 500 nm, respectively. Besides the oxide thickness, increasing the Cr and Ni content also reduces the overall weight change of the alloys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".