Effect of Amine Structure on the Corrosivity of a Carbon Steel Natural Gas Processing Plant
Bibliographic record
Abstract
Abstract This work investigated and correlated the corrosiveness of different amines with their uniquely inherent structural characteristics, specifically focusing on the effect of alkyl chain lengths of alkanolamines and diamines, and the effect of the number of -OH groups in sterically-hindered amines. The amines studied were: monoethanolamine (MEA), methylmonoethanolamine (MMEA), ethylmonoethanolamine (EMEA) and butylmonoethanolamine (BMEA) for the effect of the alkyl chain length of the alkyl group attached to the amino group of alkanolamines, and ethylenediamine (EDA), trimethylenediamine (TMDA) and hexamethylenediamine (HMDA) for the effect of alkyl chain length in diamines. For–OH effect, the amines used were: 2-amino-2-methyl-1-propanol (AMP), 2-amino-2-ethyl-1,3-propanediol (AEPD) and 2-Amino-2-(hydroxymethyl)-1,3-propanediol (AHMPD). The results showed that corrosion rates reduced as the alkyl length increased as shown in MEA > MMEA > EMEA > BMEA. For diamines, corrosion rates also reduced as the alkyl length in between amino groups increased as iluustrated in EDA >TMDA > HMDA. These were due to increased hydrophobicity which repelled water molecules more effectively from the metal surface, thereby protecting it from molecules that can cause corrosion. For–OH, the corrosion rate reduced as the number of–OH increased as in AMP>AEPD>AHMPD. In this case, the O atom, an active adsorption site that chemically adsorbs onto the metal surface, reduces the open metal surface thereby inhibiting corrosion. The results show that amines with longer alkyl length and/or higher number of–OH group will reduce corrosion rates in gas processing plants. This information will assist designers in the selection of the appropriate amines or amine blends for use in natural gas processing plants which will minimize corrosion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".