Box-Behnken Design Approach Toward Predicting the Corrosion Response of 13Cr Stainless Steel
Bibliographic record
Abstract
13Cr stainless steel, the most commonly used oil country tubular good material with good mechanical and corrosion behavior, has the drawback of sensitivity to localized corrosion, particularly in offshore downhole environments, limiting the life span of the parts. A careful assessment of the corrosion behavior of the material can be done by the perception of the most influential environmental factors combined with the material’s intrinsic microstructure. This study aims to focus on the former, the effect of environmental factors, including pH, temperature, and chloride concentration, varying in the ranges of 4 to 7, 22°C to 80°C, and 1,000 mg/L to 22,000 mg/L, respectively, on the pitting corrosion behavior of 13Cr stainless steel. Adopting a response surface methodology, using a Box-Behnken design, a carefully designed set of corrosion tests at various combinations of the environmental factors were performed. Considering the pitting potential measured from the cyclic potentiodynamic polarization testing, as the response of each experiment, a quadratic model was developed correlating the studied environmental factors and the pitting potential values. Further analysis of the developed model was conducted through analysis of variance, followed by optimizing the model according to the highest, medium, and lowest pitting potentials. The optimized results confirmed that the best corrosion behavior occurs at approximately the lowest chloride concentration and temperature, and the highest pH value. However, contrary to the expectations, the worst corrosion response was detected at the medium temperature of 52°C, instead of the highest temperature of 80°C. It was concluded that at higher temperatures, the corrosion tends to be more uniform, resulting in the formation of a layer of corrosion products that covers the sample’s surface. The corrosion product layer acts as a barrier against the diffusion of the aggressive ions, causing deceleration of the corrosion reactions.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".