Development of machine learning techniques in corrosion inhibition evaluation of 5-methyl-1 H-benzotriazole on N80 steel in acidic media

Hai, Tao; Alenizi, Farhan A.; Thanoon, Raid D.; Sharma, Kamal; Alhomayani, Fahad; Metwally, Ahmed Sayed M.

doi:10.1016/j.mtcomm.2023.106778

Cited by 1 publication

(1 citation statement)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To assess inhibitor performance, a variety of ML algorithms have been combined and widely used, including genetic algorithms (GA), multiple linear regressions (MLR), partial least squares (PLS), ordinary least squares regressions (OLS), artificial neural networks (ANN), adaptive neural fuzzy inference systems (ANFIS), and autoregressive with exogenous inputs (ARX). With the use of seven quantum chemical descriptors, the corrosion inhibition potential of eleven thiophene derivatives was predicted by the ANN model, yielding a coefficient of determination (R2) value of 0.96 [71]. Another QSPR study was created to use a mix of non-linear GA-ANN and linear GA-PLS approaches to predict molecules produced from pyridine and quinoline with 20 QCD.…”

Section: Data-driven Forecastingmentioning

confidence: 99%

Green Corrosion Inhibitors for Iron Alloys: A Comprehensive Review of Integrating Data-Driven Forecasting, Density Functional Theory Simulations, and Experimental Investigation

Akrom

2024

JIMAT

View full text Add to dashboard Cite

This comprehensive review delves into the realm of green corrosion inhibitors for iron alloys, focusing on a thorough exploration guided by data-driven investigation, density functional theory (DFT) simulations, and experimental validation. Harnessing the potential of plant extracts, this study scrutinizes their effectiveness in mitigating corrosion in iron alloys through a multi-faceted approach. By integrating computational modeling with empirical experimentation, a deeper understanding of the inhibitive mechanisms is achieved, offering insights into their practical application. The review synthesizes findings from diverse studies, elucidating the pivotal role of DFT in predicting inhibitor behavior and optimizing their performance. Furthermore, experimental validation provides crucial validation of theoretical predictions, highlighting the synergistic relationship between simulation and real-world application. Through this journey of exploration, the review underscores the promise of green corrosion inhibitors derived from natural sources, paving the way for sustainable corrosion control practices in the realm of iron alloys.

show abstract

Section: Data-driven Forecastingmentioning

confidence: 99%

Green Corrosion Inhibitors for Iron Alloys: A Comprehensive Review of Integrating Data-Driven Forecasting, Density Functional Theory Simulations, and Experimental Investigation

Akrom

2024

JIMAT

View full text Add to dashboard Cite

show abstract

Development of machine learning techniques in corrosion inhibition evaluation of 5-methyl-1 H-benzotriazole on N80 steel in acidic media

Cited by 1 publication

References 66 publications

Green Corrosion Inhibitors for Iron Alloys: A Comprehensive Review of Integrating Data-Driven Forecasting, Density Functional Theory Simulations, and Experimental Investigation

Green Corrosion Inhibitors for Iron Alloys: A Comprehensive Review of Integrating Data-Driven Forecasting, Density Functional Theory Simulations, and Experimental Investigation

Contact Info

Product

Resources

About