Corrosion Performance of Candidate Constructional Steels under Batch-mode Hydrothermal Liquefaction (HTL) Conditions
Bibliographic record
Abstract
Abstract Hydrothermal liquefaction (HTL) of wet and waste biomass feedstocks into crude bio-oils and other chemicals has attracted particular attention in Canadian and international clean energy sectors. Until today, very little effort has been employed to address corrosion problems of HTL core components under operation, leading to a significant delay in the construction of industrial-scale HTL plants. In fact, a range of oxygenated, aggressive sulfur and/or chlorinated compounds, as well as organic acids, can be introduced during the conversion at the operating temperature range of 200–400°C, consequently creating highly corrosive environments to the reactor alloys. It is thus important to investigate the performance of alloys exposed to conversion processes to determine the cost-effective construction and long-term safe operation of the HTL plants. In this study, the corrosion resistance of two candidate austenitic stainless steels, including UNS S31000 and UNS S31603, was assessed in a batch reactor containing bamboo feedstock. The corrosion behaviors of the austenitic stainless steels were evaluated using weight change measurement methods and advanced microscopy techniques. To advance corrosion mechanistic understanding, the chemistry of conversion products was also analyzed. This paper is a summary of our most recent results obtained.
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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".