Influence of Major Operating Parameters (Temperature, Pressure, and Flow Rate) on the Corrosion of Candidate Alloys for the Construction of Hydrothermal Liquefaction Biorefining Reactors
Bibliographic record
Abstract
Hydrothermal liquefaction (HTL) is a promising thermochemical technology to convert wet biomass and biowastes into marketable bio-oils and biochemicals in an environmentally friendly manner. However, industrial deployment of this technology has been significantly hindered due to very limited materials and corrosion knowledge for the selection of appropriate alloys for the construction and long-term safe operation of HTL reactors. This study investigated the influence of operating temperature, pressure, and flow rate on the corrosion modes and extents of two candidate constructional steels (SS310 and P91) using high-temperature static autoclaves and environmental loop facilities under representative HTL conversion conditions, followed by post-mortem X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy/energy-dispersive spectroscopy, focused ion beam, and transmission electron microscopy characterizations of the formed corrosion products. The two steels experienced general and/or nodular oxidation in hot HTL water at 250–365 °C. Increasing temperature and flow rate resulted in a noticeable increase in the corrosion rate of P91. For SS310, there is critical temperature point (around 310 °C) above which its corrosion rate decreases with temperature. Increasing flow rate suppressed the nodular oxidation of SS310 and consequently led to a decrease in the corrosion rate. Increasing pressure from 9.8 to 25 MPa promoted the oxide formation on SS310 while caused an increased dissolution rate of the corrosion layer grown on P91 steel at 310 °C. In the simulated HTL conversion environments, the corrosion layer on P91 was mainly composed of magnetite (Fe3O4) and chromite (Fe3–xCrxO4), while a compact and protective inner Cr-enriched layer was formed on SS310 along with the presence of other cations. Related corrosion mechanisms were also discussed and proposed.
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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 | 0.000 | 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".