Corrosion of Carbon Steel in Petrochemical Environments
Bibliographic record
Abstract
Abstract The production of petrochemicals involves the handling, treatment and processing of hydrocarbons and organic chemicals, which are generally non-corrosive towards carbon and low alloy-steels, however these hydrocarbons can carry a number of impurities such as water, chlorides and acids that can generate corrosive conditions thereby compromising plant infrastructure. Therefore, in the petrochemical industry it is necessary to develop efficient and practical methods of corrosion control to maintain plant integrity. An electrochemical high temperature and high pressure facility is used to study the corrosion behaviour of carbon steel 1018 in several petrochemical environments. The open circuit potential is measured and the effect of water and carboxylic acid concentration studied on the initiation of corrosion/fouling on carbon steel in a C6 solvent mixture at 220 ± 5° C. A corrosion mechanism is proposed that is similar to that previously proposed in the oil industry for naphthenic acid. As the concentration of total available H+ increases, through the addition of water or carboxylic acid the amount of general and localized corrosion on the carbon steel surface increases. Electrochemical impedance spectroscopy (EIS) is also used to analyze the change of impedance at the carbon steel/solution interface. Scanning electron microscopy and energy dispersive X-ray analysis (SEM/EDX) are used to look at the nature of the deposits formed after two hours of open circuit measurement.
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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 | 0.000 | 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) | 0.001 | 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".