Analysis and Optimization of H2S Scavenger Systems Using X-Ray Fluorescence Spectroscopy
Bibliographic record
Abstract
Abstract Field methods are available for the determination of dithiazine content in spent hydrogen sulfide scavengers. While these methods are useful, accuracy of the assay and interference from other chemical species or matrix effects can limit the utility of these methodologies. Due to the limitations of these methods, alternate analytical techniques were investigated. An analytical method has been developed using x-ray fluorescent (XRF) techniques that rapidly provides accurate results for total sulfur content in these scavengers. The method uses analytical equipment commercially available for sulfur in oil analysis. Sulfur compound speciation is not possible with XRF techniques, however speciation is not a critical factor in optimization as the goal is to maximize sulfur uptake while limiting dithiazine content and available sulfur to form dithiazine. The method finds application in any other scavenger solutions where soluble sulfur species are present, for example, alkanolamines or solvent based such as polyethylene glycol methyl esters. Instrument calibration is dependent upon the matrix being analyzed; however the method is robust within similar matrices of material. Instruments used for this application are light, portable and easy to use in a laboratory environment or a field environment. The method has been applied in a variety of locations in the United States and Canada to aid in optimization of scavenger application. Verification of the accuracy of the method was confirmed by random sampling and combustion analysis of the random sample of spent scavenger at Alberta Sulphur Research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".