Catalytic H<sub>2</sub>S Conversion and SO<sub>2</sub> Production over Iron Oxide and Iron Oxide/γ-Al<sub>2</sub>O<sub>3</sub> in Liquid Sulfur
Bibliographic record
Abstract
A stirred-glass autoclave containing liquid sulfur and solid iron oxide catalyst was used to study low-tonnage sulfur recovery from H 2 S-containing gas streams. The objectives were to test the feasibility of using both liquid sulfur as a reaction medium and iron oxide as a direct oxidation catalyst for prolonged H 2 S conversion. Using a 1.60% H 2 S and 0.80% O 2 (balance N 2 ) feed gas, fresh iron oxide acted primarily as a scavenger for bulk H 2 S removal from the inlet gas stream. Following the scavenging phase, the steady-state iron oxide/sulfide was able to maintain low catalytic activity (30% conversion). The steady-state catalyst did, however, have a strong ability to generate significant amounts of SO 2 in the presence of inlet feed O 2 . Data showed that this SO 2 production resulted from the oxidation of the liquid sulfur over the steady-state iron oxide/sulfide. The rate of SO 2 formation was shown to be directly proportional to the concentration of O 2 in the inlet feed gas. Although H 2 S conversions over steady-state iron oxide/sulfide ended up being lower than expected, the ability to strictly control the amount of SO 2 generated from the system was advantageous. By incorporating γ-Al 2 O 3 into a liquid sulfur reactor containing steady-state iron oxide/sulfide, the dual-catalyst system achieved 97% conversion of the H 2 S to elemental sulfur.
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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".