Hydrogen Sulfide Corrosion of Carbon and Stainless Steel Alloys in Mixtures of Renewable Fuel Sources under Co-Processing Conditions
Bibliographic record
Abstract
Corrosion rates of steel alloys were investigated in gas oil and its mixture with waste cooking oil and animal waste lard over 1, 3, 7 and 21 days under desulfurizing condition. Co-processing conditions were attempted to simulate by batch-reactor experiment at temperatures between 200 and 300oC and pressures between 20 and 90 bar in the presence of 2 volume% hydrogen sulfide. Integral and differential corrosion rates were defined by weight losses. Intense sulfide corrosion of carbon steels was less impacted by the biomass sources. Thinner scales in gas oil was probably due to frequent cohesive failure, whereas thicker layers in biomass mixtures were allowed to form to afford limited physical protection. The high corrosion rate of low alloy steel with temperature over time is related to inefficient protection by the metal sulfide scales. Greater activation energy and enthalpy balance in the formation of activated complex is expected to reflect in thick cohesive scales. Loose layers and the less unfavorable entropy balance in the transition state did not lead to valuable barrier protection. High sulfide corrosion resistance of stainless steels is in chemical in nature markedly impacted by the biomass fuel sources and contributed especially by the acidic species. Corrosion rates increased with temperature by magnitude similar to those of carbon steels, which probably owes to the less unfavorable entropy and free energy balance between the initial and transition states of the reactants.
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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".