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

Document Type

Article

Publication Date

5-1-2024

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.

Keywords

Convolutional neural network (CNN), Corrosion inhibitor, Machine learning, Timoho leaf extract, VGG16

Publication Title

Journal of Materials Research and Technology-JMR&T

Divisions

nanotechnology

Funders

Ministry of Research and Technology of the Republic of Indonesia (RISTEK)

Volume

30

Publisher

Elsevier

Publisher Location

RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

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