A detailed reaction mechanism for elemental sulphur combustion in the furnace of sulphuric acid plants
Bibliographic record
Abstract
Abstract Elemental sulphur combustion is traditionally used to generate SO 2 to produce sulphuric acid that is consumed in chemical industries, but is now also being considered as an energy vector for power generation. Despite the fact that sulphur combustion has been practiced for decades, there are limited reaction mechanisms for its combustion chemistry, which is critical for the optimization of the sulphuric acid production process to maximize efficiency and reduce cost. In this paper, a detailed reaction mechanism is developed and validated with different sets of experimental data from lab‐scale and industrial plant studies. The reaction mechanism is used to conduct sulphur furnace simulations, where the effects of feed air/sulphur ratio and oxygen enrichment of air stream on furnace temperature and the concentrations of SO 2 , SO 3 , and O 2 are investigated. The dominant reaction pathways in sulphur combustion, particularly for the production of SO 2 and SO 3 , are identified. It was found that the feed air/sulphur ratio monitors the furnace temperature and can be used to obtain the desired O 2 /SO 2 ratio at the furnace exit for the optimal operation of catalytic converter (for SO 2 oxidation to SO 3 ) that follows the furnace in the sulphuric acid plant. Moreover, high oxygen enrichment above 35% (while maintaining the desired O 2 /SO 2 ratio at the furnace exit) significantly increased the furnace capacity through reduced total gas flow (thus decreasing blower energy requirement and equipment size). The developed reaction mechanism provides a method to obtain optimized furnace parameters to achieve high efficiency and reduced costs in sulphuric acid plants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".