Machine learning modeling of time-dependent corrosion rates of carbon steel in presence of corrosion inhibitors. (December 2021)

• Record Type: Journal Article
• Authors: Aghaaminiha, Mohammadreza; Mehrani, Ramin; Colahan, Martin; Brown, Bruce; Singer, Marc; Nesic, Srdjan; Vargas, Silvia M.; Sharma, Sumit
• Is Part Of: Corrosion science. Volume 193(2021)
• Publication Date: 2021-12
• Subjects: Corrosion inhibitors -- Corrosion rates -- Machine learning -- Random forest; Corrosion and anti-corrosives -- Periodicals; 620.11223
• DOI: 10.1016/j.corsci.2021.109904
• Languages: English
• ISSNs: 0010-938X

Abstract

Abstract: We have employed supervised machine learning methods to model measurements of corrosion rates of carbon steel as a function of time when corrosion inhibitors are added in different dosage and dose-schedules. The experiments show that the time-profile of corrosion rates depend on the dose schedule, while the final rates depend mainly on the environment severity. We find that Random Forest was the best algorithm that predicted the entire time-profile of corrosion rates with the mean squared error ranging from 0.005 to 0.093. Sensitivity of corrosion rates to changes in the environmental variables are well-predicted by the trained Random Forest model. Highlights: Supervised ML models are used to model time-dependent corrosion rates of coupons. Experimental data includes single or a dose schedule of corrosion inhibitors. Random forest is able to accurately predict the entire time-profiles of corrosion rates. Sensitivity of corrosion rates to changes in solution conditions is correctly predicted.