KnE Engineering

ISSN: 2518-6841

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Prediction of Corrosion Inhibition Efficiency Based on Machine Learning for Pyrimidine Compounds: A Comparative Study of Linear and Non-linear Algorithms

Published date: Mar 07 2024

Journal Title: KnE Engineering

Issue title: Jakarta International Conference on Multidisciplinary Studies towards Creative Industries (JICOMS)

Pages: 68–77

DOI: 10.18502/keg.v6i1.15350

Authors:

Wise Herowati

Wahyu Aji Eko Prabowo - prabowo@dsn.dinus.ac.id

Muhamad Akrom

Totok Sutojo

Noor Ageng Setiyanto

Achmad Wahid Kurniawan

Novianto Nur Hidayat

Supriadi Rustad

Abstract:

The corrosion of materials poses a significant challenge in various industries, leading to substantial economic impacts. In this context, pyrimidine compounds emerge as promising, non-toxic, cost-effective, and versatile corrosion inhibitors. However, conventional methods for identifying such inhibitors are typically time-consuming, expensive, and labor-intensive. Addressing this challenge, our study leverages machine learning (ML) to predict pyrimidine compounds corrosion inhibition efficiency (CIE). Using a quantitative structure-property relationship (QSPR) model, we compared 14 linear and 12 non-linear ML algorithms to identify the most accurate predictor of CIE. The bagging regressor model demonstrated superior performance, achieving a root mean square error (RMSE) of 5.38, a mean square error (MSE) of 28.93, a mean absolute error (MAE) of 4.23, and a mean absolute percentage error (MAPE) of 0.05 in predicting the CIE values for pyrimidine compounds. This research marks a significant advancement in corrosion science, offering a novel and efficient ML-based approach as an alternative to traditional experimental methods. It shows that machine learning can quickly and accurately determine how well organic chemical inhibitors like pyrimidine stop material corrosion. This method gives the industry a new perspective and a workable solution to a problem that has existed for a long time.

Keywords: machine learning, corrosion inhibition, pyrimidine, QSPR model, predictive analysis

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