Ensemble Stacking of Machine Learning Approach for Predicting Corrosion Inhibitor Performance of Pyridazine Compounds

Authors

  • Noval Ariyanto Dian Nuswantoro University
  • Harun Al Azies Dian Nuswantoro University
  • Muhamad Akrom Dian Nuswantoro University

DOI:

https://doi.org/10.59395/ijadis.v5i2.1346

Keywords:

Corrosion, Pyridazine, Machine Learning, Stacking Ensemble, Regression

Abstract

Corrosion is a major challenge affecting various industrial sectors, leading to increased operational costs and decreased equipment efficiency. The use of organic corrosion inhibitors is one of the promising solutions. This study applies an ensemble algorithm with a stacking method to estimate pyridazine-derived compounds corrosion inhibition efficiency. This study utilized various molecular characteristics of pyridazine compounds as inputs to predict inhibition efficiency values. After evaluating several boosting models, the stacking technique was chosen as it showed the best results. Stacking Model 6, which combines XGB, LGBM, and CatBoost as the base model with Random Forest as the meta-model, produced the most accurate prediction with an RMSE of 0.055. These findings indicate that machine learning approaches can effectively and efficiently predict corrosion inhibitor performance. This method offers a faster and more economical alternative to conventional experimental methods.

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Published

2024-11-03

How to Cite

Ensemble Stacking of Machine Learning Approach for Predicting Corrosion Inhibitor Performance of Pyridazine Compounds. (2024). International Journal of Advances in Data and Information Systems, 5(2), 198-215. https://doi.org/10.59395/ijadis.v5i2.1346

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