High-Temperature Corrosion of Unpassivated Carbon Steel in Simulated Boiler Water: Electrochemical Thresholds and Mechanisms for Chloride and Sulphate Contamination
Bibliographic record
Abstract
The presence of chloride (Cl-) and sulphate (SO42-) in boiler water poses a significant threat to carbon steel boiler tube integrity, yet their specific corrosion mechanisms and interaction at industrially relevant high temperatures remain poorly characterized. This study quantifies the accelerated corrosion thresholds and elucidates the distinct mechanistic roles of these contaminants for initially unpassivated SA210-A1 carbon steel exposed to simulated all-volatile treatment (AVT) boiler water at 310 °C and 10.3 MPa. In situ corrosion rates were measured using linear polarization resistance (LPR), supported by thermochemical equilibrium modelling (FactSage™). Results show that the corrosion threshold for SO42- is approximately 1.5 to 3 times higher than for Cl-, validating industry heuristics. Mechanistic analysis indicates that Cl- is associated with lowering the solution pH which in turn increases iron solubility, while sulphate serves as an electron-accepting species under the high-temperature reducing conditions at 310 °C, where hydrothermal reduction to H2S is thermodynamically favoured. Furthermore, the chemical form in which the contaminants were introduced strongly governs their combined behaviour. When introduced as ammonium salts, the combined threshold was higher than for individual Cl- contamination, while mineral acid (HCl and H2SO4) additions resulted in a lower combined contamination threshold than that of Cl- alone. Since ammonium salts do not reflect the acidic, deposit-controlled environments expected in operating boilers, mineral acid additions provide a more realistic representation of localized chemistry. These findings provide quantitative thresholds and mechanistic clarity that establish a foundational basis for improving water chemistry guidelines and models.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".