Corrosion Behavior of Carbon Steel in the Monoethanolamine−H<sub>2</sub>O−CO<sub>2</sub>−O<sub>2</sub>−SO<sub>2</sub> System
Bibliographic record
Abstract
The effects of operating parameters on the corrosion of carbon steel in the monoethanolamine (MEA)−H 2 O−CO 2 −O 2 −SO 2 system were investigated using two different corrosion measurement techniques. The corrosion studies were conducted using a 273A potentiostat using MEA, O 2, and SO 2 concentrations and CO 2 loading in the ranges of 1−7 kmol/m 3, 0−100%, 0−204 ppm, and 0−0.5 mol CO 2 /mol MEA, respectively, at corrosion temperatures in the range of 303−353 K. The experimental results showed, for the first time, that a higher concentration of SO 2 in a simulated flue gas stream induces a higher corrosion rate essentially because of the increase in the hydrogen ion concentration generated by reactions of SO 2 and H 2 O as well as SO 2, O 2, and H 2 O. Also, an increase in oxygen concentration in the simulated flue gas stream causes a higher corrosion rate due to the increasing solubility of oxygen and, in turn, a higher amount of dissolved oxygen in the liquid phase. The results further show that an increase in the concentrations of MEA, O 2, and SO 2 as well as CO 2 loading will cause the generation of higher amounts of hydrogen or hydronium ions, as well as carbonic acid and bicarbonate ions, and this is what leads to a higher corrosion rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".