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

Open

Femiana Gapsari, Fitri Utaminingrum, Chin Wei Lai, Khairul Anam, Abdul M. Sulaiman, Muhamad F. Haidar, Tobias S. Julian, Eno E. Ebenso

2024 Journal of Materials Research and Technology Vol. 30 Article Cited by 37 Quartile

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. © 2024 The Authors

Affiliations

Department of Mechanical Engineering, Faculty of Engineering, Brawijaya University, MT Haryono 167, Malang, 65145, Indonesia; Computer Vision Research Group, Faculty of Computer Science Brawijaya University, Veteran 12-14, Malang, 65145, Indonesia; Nanotechnology and Catalysis Research Centre, Institute for Advanced Studies, Universiti Malaya, Level 3, Block A, Kuala Lumpur, 50603, Malaysia; Department of Mechanical and Industrial Engineering, Univiversitas Gadjah Mada, Jalan Grafika No. 2, Yogyakarta, 55281, Indonesia; Centre for Materials Science, College of Science, Engineering and Technology, University of South Africa, Johannesburg, 1710, South Africa; Institute for Nanotechnology and Water Sustainability, College of Science, Engineering and Technology, University of South Africa, Johannesburg, 1710, South Africa