metallic corrosion
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Molecules ◽  
2021 ◽  
Vol 26 (22) ◽  
pp. 7024
Author(s):  
Nasreen Al Otaibi ◽  
Hassan H. Hammud

Extract of natural plants is one of the most important metallic corrosion inhibitors. They are readily available, nontoxic, environmentally friendly, biodegradable, highly efficient, and renewable. The present project focuses on the corrosion inhibition effects of Peganum Harmala leaf extract. The equivalent circuit with two time constants with film and charge transfer components gave the best fitting of impedance data. Extraction of active species by sonication proved to be an effective new method to extract the inhibitors. High percent inhibition efficacy IE% of 98% for 283.4 ppm solutions was attained using impedance spectroscopy EIS measurements. The values of charge transfer Rct increases while the double layer capacitance Cdl values decrease with increasing Harmal extract concentration. This indicates the formation of protective film. The polarization curves show that the Harmal extract acts as a cathodic-type inhibitor. It is found that the adsorption of Harmal molecules onto the steel surface followed Langmuir isotherm. Fourier-transform infrared spectroscopy FTIR was used to determine the electron-rich functional groups in Harmal extract, which contribute to corrosion inhibition effect. Scanning electron microscopy SEM measurement of a steel surface clearly proves the anticorrosion effect of Harmal leaves.


Author(s):  
Leijian Yu ◽  
Erfu Yang ◽  
Cai Luo ◽  
Peng Ren

AbstractCorrosion has been concerned as a serious safety issue for metallic facilities. Visual inspection carried out by an engineer is expensive, subjective and time-consuming. Micro Aerial Vehicles (MAVs) equipped with detection algorithms have the potential to perform safer and much more efficient visual inspection tasks than engineers. Towards corrosion detection algorithms, convolution neural networks (CNNs) have enabled the power for high accuracy metallic corrosion detection. However, these detectors are restricted by MAVs on-board capabilities. In this study, based on You Only Look Once v3-tiny (Yolov3-tiny), an accurate deep learning-based metallic corrosion detector (AMCD) is proposed for MAVs on-board metallic corrosion detection. Specifically, a backbone with depthwise separable convolution (DSConv) layers is designed to realise efficient corrosion detection. The convolutional block attention module (CBAM), three-scale object detection and focal loss are incorporated to improve the detection accuracy. Moreover, the spatial pyramid pooling (SPP) module is improved to fuse local features for further improvement of detection accuracy. A field inspection image dataset labelled with four types of corrosions (the nubby corrosion, bar corrosion, exfoliation and fastener corrosion) is utilised for training and testing the AMCD. Test results show that the AMCD achieves 84.96% mean average precision (mAP), which outperforms other state-of-the-art detectors. Meanwhile, 20.18 frames per second (FPS) is achieved leveraging NVIDIA Jetson TX2, the most popular MAVs on-board computer, and the model size is only 6.1 MB.


Author(s):  
Y.V.D. Nageswar

Plants are a rich source of different varied organic compounds. Due to the important applications of naturally occurring chemicals their derivatives are also pursued for modifying and potentiating the activities of natural products. Metallic corrosion is a natural process resulting in heavy losses in various fields. Non hazardous and non toxic corrosion inhibitors gained significance due to the environmental regularities and guidelines issued in the course of saving the pristine nature of environment and to maintain the sustainability of our earth. Green corrosion inhibitors play a potential role for the above said cause. Recent research contributions on green corrosion inhibitors from the active researchers in the concerned expertise are presented briefly here to give an idea about the current research activity across the world.


2021 ◽  
pp. 103046
Author(s):  
Sheng Zhang ◽  
Xinling Deng ◽  
Yumin Lu ◽  
Shaozheng Hong ◽  
Zhengyi Kong ◽  
...  

2021 ◽  
Author(s):  
Brahim El Ibrahimi ◽  
Lei Guo ◽  
Jéssica Verger Nardeli ◽  
Rachid Oukhrib

Biopolymers-based compounds were used by different manners for metal protection toward corrosion phenomena, namely via inhibiting additive and coating strategies. In the last decade, the application of these compounds or their chemically modified forms as effective replacements for toxic inorganic and organic inhibitors attracts more attention. Additionally to their intrinsic chemical stability, biodegradability, eco-friendly, low cost and renewability, biopolymers set were shown the remarkable effect to control the dissolution of several metallic materials in various corrosive environments. Among a large variety of available biopolymers, chitosan and its functionalized form, as well as its nanoparticle composites, have been reported and widely used as good anti-corrosion compounds for different metal/medium systems. In this context, the current chapter aims to shed more light on this subject.


2021 ◽  
Vol 23 (1) ◽  
pp. 11-20
Author(s):  
Xiaofei Cui ◽  
Xiaoxia Liang ◽  
Ujjwal Bharadwaj

Metallic corrosion is a big challenge affecting many sectors in a nation’s economy. Necessary corrosion prevention actions have to be taken in order to maintain the integrity of engineering assets susceptible to corrosion. This paper proposes a holistic framework to support the management of corrosion in metallic structures. It is a fully automation corrosion assessment process, with risk updated by Bayesian theory. Through analyzing the thickness data measured by non-destructive testing (NDT) techniques, the influence of corrosion on the component can be estimated using statistical methods, which will enable users to make decisions on maintenance based on quantitative information. A case study using corrosion data from a steel bridge is included to demonstrate the proposed framework. It improved the conventional corrosion analysis method by the proposed statistical approach using representative thickness data, which aims to take full use of the remaining life. This model can be adapted to a wide range of metallic structure suffering from corrosion damage.


Author(s):  
Y. Ono ◽  
A. Tsuji ◽  
J. Abe ◽  
H. Noguchi ◽  
J. Abe

Abstract. We have developed an automatic detection method for metallic corrosion in facilities by using a LiDAR point cloud. While visual inspections for monitoring facilities are widely conducted, the inspection result depends on human skill, and there is currently a shortage of inspectors. While automatic detection methods using an RGB image have been developed, such methods cannot be applied to inspections at night. Therefore, we propose a robust detection method that utilizes both 3D shapes and intensities in a LiDAR point cloud instead of RGB information. The proposed method segments the point cloud into a basic building material by using the 3D shape and then recognizes a point cloud with an abnormal intensity in each material as the corrosion area. We demonstrate through experiments that the proposed method can robustly detect corrosion spots in aging facilities during detection conducted both during the day and at night.


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