Corrosion Performance of Reactor Candidate Alloys During Hydrothermal Liquefaction (HTL) of Cellulose in a Batch Reactor
Bibliographic record
Abstract
Abstract Hydrothermal liquefaction (HTL) is seen as a promising thermochemical approach to convert wet and waste biomass feedstocks into biocrude oils and other valuable chemicals. One of the critical technical barriers that must be addressed for the industrial deployment of HTL technology is the corrosion of process core equipment, especially the refining reactors, due to the presence of the hot-compressed water medium, applied alkali catalyst, and aggressive intermediate and final products (such as aggressive sulfur and/or chlorinated compounds, organic acids) generated during the conversion. In this study, the corrosion performance of two candidate alloys (UNS N06625 and UNS R20033) was investigated in a batch reactor containing hot-compressed water, 5 wt.% K2CO3 catalyst and cellulose (a typical model compound of lignocellulosic biomass). Certain amounts of organic acids and phenolic compounds were present in the produced oil, implying the change of environmental pH (from mild basic to near neutral) during the conversion. The two tested alloys experienced general oxidation associated with localized oxide peel-off or nodular oxidation. Due to its higher Cr content, UNS R20033 had a lower corrosion rate compared to UNS N06625 under the HTL of cellulose.
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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".