Key Processing Factors in Hydrothermal Liquefaction and Their Impacts on Corrosion of Reactor Alloys
Bibliographic record
Abstract
Despite intensive efforts to develop hydrothermal liquefaction for the conversion of wet biomass and biowaste feedstocks into valuable bio-oils, severe corrosion of conversion reactor alloys and other core components, induced by the pressurized hot water medium, catalysts, and inorganic and organic corrodants generated during the conversion process, has significantly hindered the industrial deployment of this attractive technology. In this paper, a general review of major operating parameters, including biomass feedstock types, temperature, pressure, and catalysts, was conducted to advance the understanding of their roles in conversion efficiency and the yield and properties of produced oils. Additionally, the corrosion performance of a representative constructional alloy (Alloy 33) was investigated in both non-catalytic and catalytic HTL environments at temperatures of 310 °C and 365 °C, respectively. The alloy experienced general oxidation in the non-catalytic HTL environment but suffered accelerated corrosion (up to 4.2 µm/year) with the addition of 0.5 M K2CO3 catalyst. The corrosion rate of the alloy noticeably increased with temperature and the presence of inorganic corrodants (S2− and Cl−) released from biowastes. SEM/XRD characterization showed that a thin and compact Cr-rich oxide layer grew on the alloy in the non-catalytic HTL environment, while the surface scale became a double-layer structure, composed of outer porous Fe/Cr/Ni oxides and inner Cr-rich oxide, with the introduction of the K2CO3 catalyst. From the corrosion perspective, the alloy is a suitable candidate for construction in the next phase of pilot-scale validation assessment.
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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 | 0.001 |
| 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 | 0.001 | 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".