Shot Peening of SS 316L Hydrometallurgy Tanks to Reduce Chloride Stress Corrosion Cracking Susceptibility
Bibliographic record
Abstract
Abstract The stainless steel tanks of a zinc hydrometallurgy plant have experienced stress corrosion cracking (SCC) since they were built 20 years ago. The diluted sulphuric acid solution with 300 ppm chloride ions is known to be particularly aggressive. In order to reduce the susceptibility to stress corrosion cracking, the repair procedures, including welding and passivation, were optimized. Since it is sometimes suggested in the literature that compressive surface residual stresses could reduce the susceptibility to stress corrosion cracking,1 this hypothesis was first tested in the laboratory by shot peening 316L welded and stressed samples that were later immersed in boiling MgCl2 according to the ASTM G362 standard. Following these tests, some samples were immersed for 3 months in a small tank in a laboratory in conditions as close as possible to the service conditions. Finally, the technique was tested in the field by shot peening (with glass beads) a 316L tank that had undergone repairs. The shot peening parameters were carefully selected so that the depth of the compressive layer was sufficient, without inducing exceedingly high tensile stress deeper in the shell. The laboratory tests clearly showed that shot peening was effective at preventing stress corrosion cracking. The field test showed that, while shot peening prevented the initiation of stress corrosion cracks on the inside of the tank shell, it did not reduce the occurrence of pitting. Since the bottom of the pits eventually reached a depth (below the shot-peened compressive stress layer) where the tensile stress was high and also acted as a stress concentrator, stress corrosion cracks were initiated at the bottom of the pits and propagated through the shell of the tank.
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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) | 0.002 | 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".