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Data-Driven Modeling of Molten Chloride Corrosion Using Static Test Data — arXiv:2611.07125

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.07125
License (as archived): CC0-1.0
Archived: 2026-11-06T14:44:05.074Z
SHA-256 of the archived text: 8d6e32803a07674dc16cfdf3aa77221e68687358461a400b17d6a792997f9a76
Stable URL of this page: https://metraipolis.com/documento/dc_000ff46619ca

How to cite

Data-Driven Modeling of Molten Chloride Corrosion Using Static Test Data — arXiv:2611.07125 — arXiv (en), https://arxiv.org/abs/2611.07125 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_000ff46619ca (Metraipolis archive dc_000ff46619ca, sha256 8d6e32803a07674dc16cfdf3aa77221e68687358461a400b17d6a792997f9a76)

BibTeX

@misc{dc000ff46619ca,
  title = {Data-Driven Modeling of Molten Chloride Corrosion Using Static Test Data — arXiv:2611.07125},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.07125},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_000ff46619ca; sha256 8d6e32803a07674dc16cfdf3aa77221e68687358461a400b17d6a792997f9a76},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.07125. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Molten chloride corrosion has been studied for decades, yet reliable prediction across different alloys, salts, and test conditions remains difficult. In this work, 291 static molten chloride corrosion experiments reported in the literature were compiled into a structured dataset and used to develop an interpretable machine-learning framework for corrosion-rate predictions. To better represent the differences in salt condition across studies, a Chloride Salt Preparation Index (CSPI) was introduced to bring the reported salt-treatment method, atmosphere control, and pre-exposure impurity condition on a common numerical scale. A blended tree-based ensemble provided the most consistent predictive performance, achieving a cross-validated \(R^2\) of 0.85 and an MAE of 0.41 mg cm\(^{-2}\) day\(^{-1}\). The graded CSPI retained more useful information than a simple purified/unpurified classification and emerged as the dominant model descriptor. Global sensitivity and SHAP analyses further identified exposure time, temperature, and alloy chemistry as important contributors to the model predictions, with several observed trends consistent with the established molten-chloride corrosion behavior. The conformal prediction intervals were well calibrated, providing an estimate of prediction uncertainty in addition to the point prediction. Overall, the framework demonstrates that historical corrosion data can support quantitative, interpretable, and uncertainty-aware prediction when experimental context is represented explicitly, while also highlighting where more consistent reporting and targeted experiments are needed to improve future molten-salt corrosion models.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Materials Science (cond-mat.mtrl-sci).
Pagina del lavoro: https://arxiv.org/abs/2611.07125 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-06T14:21:42.613Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).