A convolutional neural network-VGG16 method for corrosion inhibition of 304SS in sulfuric acid solution by timoho leaf extract

• Main Authors: Gapsari, Femiana, Utaminingrum, Fitri, Lai, Chin Wei, Anam, Khairul, Sulaiman, Abdul M., Haidar, Muhamad F., Julian, Tobias S., Ebenso, Eno E.
• Format: Article
• Published: Elsevier 2024
• Subjects: Q Science (General); QD Chemistry
• Online Access: http://eprints.um.edu.my/45263/; https://doi.org/10.1016/j.jmrt.2024.03.156
• Institution: Universiti Malaya

A corrosion inhibition test, coupled with a quantification of in-situ H2 evolution, can be used to evaluate an organic inhibitor such as Timoho leaf extract (TLE). TLE is a biodegradable and effective corrosion inhibitor because of its potential to protect 304SS against sulfuric acid. TLE corrosion inhibitor was studied through systematic electrochemical experiments and morphological characterization, with a concentration range of 0-6g L-1. Convolutional Neural Network (CNN)-VGG16 was one of the machine learning approaches used to classify and predict physical changes in hydrogen gas bubbles. Constituents of the TLE and 304SS surfaces were analyzed by FT-IR and UV-Vis tests. The results suggested that 3 g L-1 TLE inhibitor was able to reduce the corrosion rate by 99.37 %. The TLE's inhibition mechanism on 304SS was mixed adsorption and mixed type inhibitor that followed the Isothermal Freundlich Equation. The prediction model by CNN-VGG16 for corrosion tests at varied inhibitor doses was 96% accurate. SEM tests revealed that TLE constituent adsorption on the 304SS surface had a smooth surface morphology with few degraded spots.