Formaldehyde-MEA Triazine Based Hydrogen Sulfide Scavenger Behavior Study and Applications in the Oil and Gas Industry
Bibliographic record
Abstract
Hydrogen sulfide (H2S) is an environmentally hazardous, corrosive and toxic gas in oil and gas productions. It must be removed to meet the pre-determined specification. There are many chemicals being used in the gas sweetening process. Formaldehyde-MEA Triazine is one of the common H2S scavengers in use. The objective of this research was to review different chemistries and field applications applicable as H2S scavengers in oil and gas, understand the Formaldehyde-MEA Triazine’s properties and behaviors in the sweetening process, and determine technical achievements on custom-made finished blends for successful field applications. The Formaldehyde-MEA Triazine is synthesized by the reaction of formaldehyde and monoethanolamine (MEA). Triazine, water, free formaldehyde or free MEA are fundamental components. Each ingredient has its unique function in process scavenging H2S. There are three steps for triazine’s chemical reaction absorbing H2S. Free formaldehyde can boost the capacity and breakthrough efficiency. However, it makes triazine degradation for shorter shelf life as well. A sufficient water amount, minimum 40% more than triazine, is a must to avoid pre-mature breakthrough in bubble tower uses. Triazine based H2S scavengers are custom made blends for field use. Besides the Formaldehyde-MEA Triazine, other factors such as the solvent package, other additives, plant synthesis, low temperature operation, spent solids treatment, sour liquid sweetening and capacity monitoring are all important considerations for successful H2S sweetening applications in the western Canadian oil and gas industry.
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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 | 0.000 |
| 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) | 0.002 | 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".