Analysis of corrosion inhibition of Kleinhovia hospita plant extract aided by quantification of hydrogen evolution using a GLCM/SVM method

Sanjay, M. R.; Darmadi, Djarot B.; Setyarini, Putu Hadi; Wijaya, Hastono; Madurani, Kartika A.; Juliano, Hans; Sulaiman, Abdul M; Hidayatullah, Syarif; Tanji, Ayoub; Hermawan, Hendra

doi:10.1016/j.ijhydene.2023.01.067

Cited by 24 publications

(6 citation statements)

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“…The images were preprocessed to extract GLCM-based features and sent to a classifier such as a support vector machine. The research by Gapsari et al [17] analyzes the corrosion inhibition efficiency of carbon steel specimens based on images characterized using a gray-level co-occurrence matrix (GLCM) and a support vector machine. Darnton and Ruzzene [18] employ phase congruency processing to visualize the extent of subsurface damage in aluminum structures.…”

Section: Bibliographic Reviewmentioning

confidence: 99%

Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing

Ruelas-Santoyo,

Ríos-Lira,

Pantoja-Pacheco

et al. 2024

Applied Sciences

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The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate progressive wear that must be monitored from the perspective of the microstructure of the material. When AISI 304 stainless steel is subjected to a temperature range between 450 and 850 °C, it is susceptible to intergranular corrosion. This phenomenon, known as sensitization, causes the material to lose strength and generates different patterns in its microstructure. This research analyzes three different patterns present in the microstructure of stainless steel, which manifest themselves through the following characteristics: the absence of intergranular corrosion, the presence of intergranular corrosion, and the precipitation of chromium carbides. This article shows the development of a methodology capable of recognizing the corrosion patterns generated in stainless steel with an accuracy of 98%, through the integration of a multilayer perceptron neural network and the following digital image processing methods: phase congruence and a gray-level co-occurrence matrix. In this way, an automatic procedure for the analysis of the intergranular corrosion present in AISI 304 stainless steel using artificial intelligence is proposed.

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Section: Bibliographic Reviewmentioning

confidence: 99%

Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing

Ruelas-Santoyo,

Ríos-Lira,

Pantoja-Pacheco

et al. 2024

Applied Sciences

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“…Figure 2b shows the minicell was linked to the three-electrode cell within the solutions contained in the specimen container [6]. After closure, it was directed toward a 50 mL syringe designed for dissolved gas analysis (DGA) (Figure 2b), as well as to the solutions.…”

Section: Electrochemical Corrosion Testingmentioning

confidence: 99%

“…Corrosion inhibitors can be produced from organic and non-organic compounds. The advantages of organic compounds are they are non-toxic, affordable, renewable, and easy to produce [5,6] but still need a lot of research to fully understand their performance and mechanism. On another side, non-organic inhibitors have already been studied and tested [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Detecting the H 2 bubbles at a certain corrosion rate condition is not an easy job, for instance, in scenarios characterized by low current density [34,39,40]. To detect bubbles from a corrosion process in a metal weldment image, a machine learning algorithm (GLCM/SVM method) was implemented [6]. In the deep learning concept, the computer will attempt to map a feature from a set of images through a training process to acquire certain patterns, for example, object sizes, color channels, and so on, to support object detection schemes [41,42].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Hydrogen Bubbles Produced by Corrosion Inhibition of Metal Weldment Using Machine Learning

Alamsyah,

Cheng,

Gapsari

2023

Applied Sciences

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This study analyzes the corrosion inhibition efficiency of sodium molybdate (SM) solution on weldment specimens in 1 M HCl, based on H2 bubbles detection. The detection of the bubbles produced by the corrosion process is achieved by a YOLOv4 deep learning algorithm. The results indicate that the corrosion rate is higher on the weld metal zone than on the base metal zone in the same solution, which might be attributed to the coarser grain of the weld metal zone and the stability of the produced oxide layer. The addition of sodium molybdate was discovered to boost the stability of the oxide layer, hence enhancing the specimens’ corrosion resistance. The overall inhibitory efficiency of the sodium molybdate solution was 59% for the weld metal (WM) (0.4 g/L), 52% for the heat-affected zone (HAZ) (0.2 g/L), and 37% for the base metal (BM) (0.2 g/L). The object detection algorithm models showed 97% for the mAP and 0.98 for recall. The minimum average bubble detected for the WM was 0.353 /mm2 at an SM concentration of 0.4 g/L, while the HAZ was 0.612 /mm2 at 0.2 g/L, and the BM was 1.055 /mm2 at 0.2 g/L. The results of the bubbles detection appeared to be consistent with the corrosion experiment outcomes obtained by the potentiodynamic polarization and hydrogen volume measurement tests. This experiment validates the distinctiveness of the different weld zones in terms of the inhibitor concentration required for successful corrosion prevention, as well as the potential of analyzing corrosion using machine learning algorithms for object detection.

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“…This paper is to study the optimum concentration of camellia sinensis extract as corrosion inhibitor in a 1M HCl environment, on Aluminium 6061, by utilizing supervised machine learning model. Machine learning models have been used widely in various applications such as predicting human fatigue [20] and perceived workload [21], architectural material selections [22] and also in predicting corrosion and analysing inhibitor effectivity on metals [23], [24]. In corrosion studies, the artificial neural networks (ANN), was proven effective to analyze corrosion inhibition of 304SS in sulfuric acid solution using timoho leaf extract [23], which is considerable to be used in this study.…”

Section: Introductionmentioning

confidence: 99%

Analysis of The Corrosion Inhibition Efficacy of Camellia Sinensis Extract on Aluminium 6061 Alloys using Artificial Neural Network (ANN)

Andrean,

Harmayanti,

Wahjudi

et al. 2024

memi

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Aluminum alloys, well known for their corrosion resistance, could encounter corrosion issues in acidic environments. These environments induced pitting and exfoliation corrosion due to the absence of oxide layers from such alloys. Camellia sinensis extract, one of the organic extract, had inhibition compounds, including tannins and cathecins, that could inhibit corrosion and reduce the corrosion rate. Hence, this study investigated the corrosion inhibition efficacy of Camellia sinensis extract on Aluminum 6061 alloys when exposed to an acidic environment (1M HCl). Different concentrations of the extract were prepared to obtain the optimum concentration and achieve the highest inhibitor efficiency. In addition, an artificial neural network (ANN) model, was employed to predict polarization current in both inhibited and unhibited solutions. The model was designed using a configuration of one input, six hidden and one output ;ayers. The study discovered that the efficiency reached a remarkable level of 82.68% when using a concentration of 3000 ppm of Camellia sinensis extract. Furthermore, the ANN model demonstrated excellent performance in predicting polarization current across all variations, with determinant coefficient (R2) values of 0.995 for 0 ppm, 0.990 for 1000 ppm, 0.997 for 2000 ppm, 0.9996 for 3000 ppm, and 0.996 for 4000 ppm. These results indicated that the model was reliable in simulating the electrochemical analysis of corrosion behavior which could be used to develop a corrosion rate predictor in the future.

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Analysis of corrosion inhibition of Kleinhovia hospita plant extract aided by quantification of hydrogen evolution using a GLCM/SVM method

Cited by 24 publications

References 50 publications

Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing

Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing

Detection of Hydrogen Bubbles Produced by Corrosion Inhibition of Metal Weldment Using Machine Learning

Analysis of The Corrosion Inhibition Efficacy of Camellia Sinensis Extract on Aluminium 6061 Alloys using Artificial Neural Network (ANN)

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