Harnessing Machine Learning for QSPR Modeling of Corrosion Inhibitors in HCl for Mild Steel Protection

Idrissi M. B., Moumen I., Taghzouti S., SAYIN K., Chakir E. M., Zarrok H., ...Daha Fazla

Current Analytical Chemistry, cilt.21, sa.4, ss.356-373, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 21 Sayı: 4
  • Basım Tarihi: 2025
  • Doi Numarası: 10.2174/0115734110312696240822101941
  • Dergi Adı: Current Analytical Chemistry
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Biotechnology Research Abstracts, Chemical Abstracts Core
  • Sayfa Sayıları: ss.356-373
  • Anahtar Kelimeler: corrosion inhibitors, machine learning, mild steel, multilayer perceptron regression, QSPR, random forest regression, support vector regression, XGBoost
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

Background: The corrosion of Mild Steel (MS) in harsh acidic environments, such as Hydrochloric acid (HCl), is a significant industrial issue with environmental consequences. Corrosion inhibitors, particularly those containing heteroatoms and aromatic rings, are a proven method for mitigating corrosion. Traditional methods for studying corrosion inhibitors often require resource-intensive experiments. Methods: This study explores the use of Quantitative Structure-Property Relationship (QSPR) modeling, a Machine Learning (ML) technique, to predict the inhibition efficiency of organic corrosion inhibitors in HCl environments. Several ML models were employed: Linear Regression (LR), Random Forest Regression (RF), Support Vector Regression (SVR), Multilayer Perceptron Regression (MLP), and XGBoost Regression (XGB). Results: The investigation revealed that some models achieved exceptional predictive accuracy with significantly reduced errors and high precision. These models offer a promising avenue for efficient corrosion inhibitor design, reducing reliance on extensive experimentation. Conclusion: This study contributes to the advancement of corrosion science and materials engineering by introducing innovative strategies for developing effective corrosion inhibitors using machine-learning-driven QSPR models.