A convolutional neural network -VGG16 method for corrosion inhibition of 304SS in sulfuric acid solution by timoho leaf extract
Femiana Gapsari, Fitri Utaminingrum, Chin Wei Lai, Khairul Anam, Abdul Mudjib Sulaiman, Muhamad F. Haidar, Tobias S. Julian, Eno E. Ebenso
Abstract
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.