Advanced Oxygen Enrichment Technology for Cost Effective Sulfur Recovery Processing Facility Applications in China
Bibliographic record
Abstract
Abstract A global trend towards increasingly-stringent environmental regulations of sulfur dioxide emissions to improve air quality is faced. China has been adopting progressive policies for improvement of air quality. Recent industry trends focused on producing cleaner air and fuels around the globe, especially in China, have generated significant demand for additional hydro-desulfurization and sulfur recovery capacities in both new and existing refineries and gas plants. Oxygen enrichment technology frequently offers the most economical route to achieve the desired increase in sulfur processing capacity with high recovery efficiency. This commercially proven technology has excellent operating safety records as witnessed by the safe operation of over 300 SRU/TGTU plants in USA, Canada, Europe, Middle East, South Africa and a newly installed facility in Panjin, China. This paper provides a technical background of oxygen enrichment technology, and discusses the various economic, logistical, process and operational advantages that can be realized through its implementation.
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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 | 0.001 | 0.001 |
| 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".