Gemini surfactant corrosion inhibitor (CI) is one type of CI mainly used in mitigating corrosion in the complex system of oil/gas production industries. Computer modeling methods such as density functional theory (DFT) calculation and molecular dynamic (MD) simulation are required to develop new CI molecules focusing on their application condition as a prediction or screening process before the physical empirical assessment. In this work, the adsorption inhibition efficiencies of two monomer surfactants (2B and H) and their respective Gemini structures with the addition of different spacers (alkyl, benzene, ester, ether, and ketone) are investigated using DFT calculation and MD simulation method in 3% sodium chloride (NaCl), and 1500 ppm acetic acid solutions. In DFT calculation, 2B-benzene molecules are assumed to have the most promising inhibition efficiency based on their high reactivity and electron-donating ability at their electron-rich benzene ring region based on the lowest bandgap energy (0.765 eV) and highest HOMO energy value (−2.879 eV), respectively. DFT calculation results correlate with the adsorption energy calculated from MD simulation, where 2B-benzene is also assumed to work better as a CI molecule with the most adsorption strength towards Fe (110) metal with the highest negative adsorption energy value (−1837.33 kJ/mol at temperature 323 K). Further, diffusion coefficient and molecular aggregation analysis in different CI concentrations through MD simulation reveals that only a small amount of Gemini surfactant CI is needed in the inhibition application compared to its respective monomer. Computer simulation methods successfully predict and screen the Gemini surfactant CI molecules that can work better as a corrosion inhibitor in acetic acid media. The amount of Gemini surfactant CI that needs to be used is also predicted. The future planning or way forward from this study will be the development of the most promising Gemini surfactant CI based on the results from DFT calculation and MD simulations.

In recent years, numerous research work has been conducted in order to find the most practical and cost-effective solution for corrosion issues in the oil and gas industry. Several studies have revealed that fatty hydrazide derivatives are the most suitable corrosion inhibitor for the application in the said industry. These compounds can also act as effective corrosion inhibitors in acidic medium with inhibition efficiency greater than 95%. This review summarizes and discusses the recent corrosion inhibitor development in acidic media from 2017 until 2021, focusing on fatty hydrazide derivatives. The significant findings and mechanisms of inhibition have been elucidated. In addition, intake on the computer simulation studies of fatty hydrazide inhibition properties is also included in this review. Finally, some suggestions for future research on corrosion inhibitors have been recommended.

Corrosion in metallic materials is a critical challenge in maintenance and safety, and traditional visual inspection methods are often time-consuming, labor-intensive, and dependent on human expertise, highlighting the need for more efficient and reliable solutions. Deep learning, particularly convolutional neural networks (CNNs), provides a promising approach by enabling automated and accurate image-based classification. This study investigates binary image classification of corrosion using four pre-trained CNN architectures, namely ResNet50, MobileNetV2, NASNetMobile, and EfficientNetV2B0, and integrates explainable artificial intelligence (XAI) techniques to provide interpretability and insight into each model’s decision-making process. A curated dataset of 4012 images, divided between corroded and non-corroded surfaces, was pre-processed, and augmented images resulted in a total of 9636 images used to train and evaluate the models. Performance was assessed through accuracy, confusion matrices, computational timing, receiver operating characteristic curves, precision–recall curves, and Cohen’s Kappa. In this paper, Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations are incorporated as an XAI technique to provide interpretable insight into the model’s reasoning process, enabling clear identification of corrosion regions and offering justification for each prediction produced by the system. A key contribution of this work is the integration of Grad-CAM to enhance explainability. The results showed that EfficientNetV2B0 demonstrates stable training with minimal sign overfitting compared to other models. MobileNetV2 achieved the lowest time to train with the datasets given, and ResNet50 achieved the highest classification performance in terms of confusion matrix, with an accuracy of 96.58%. Through Grad-CAM reasoning, EfficientNetV2B0 shows a specific high activation towards corroded regions compared to the other three models that were evaluated.

More aggressive reservoir conditions, such as higher concentrations of CO2 corrosive gases, have a massive effect on corrosion. Moreover, additional aqueous species, such as organic acids, have complicated the conditions and enhanced corrosion to the maximum rate. This study investigates and screens three commercial corrosion inhibitors on their performance in mitigating corrosion at welded areas of carbon steel in high CO2 conditions with the presence of organic acids. These investigations were conducted via chemical elucidations to study the functional groups present in the compounds that affect the inhibition performance and the electrochemical measurements to evaluate the inhibition activities and efficiency of these commercial inhibitors under extreme conditions of high CO2 and organic acids. The results indicated that only CT5727 and CT17906 gave a high inhibition efficiency of 90% in the absence and presence of organic acid, with CT5727 giving the lowest corrosion rate of 0.038 mmpy in the absence of organic acid, while CT17906 gave the lowest corrosion rate of 1.00 mmpy in organic acid condition. On the other hand, D4305 gave the worst inhibition performance under both conditions. The elucidation of these inhibitors also found that specific functional groups such as hydroxy, carboxylic acid, alkyl and alkene presence in the structure were believed to affect the inhibition performance of these commercial inhibitors in mitigating corrosion. These could be used as a future reference for the new development of corrosion inhibitors in similar conditions.

Experimental investigations were carried out to study the influence of heat treatment on the corrosion behaviour of API 5L X65 pipeline steel in the presence of acetic acid HAc and carbon dioxide CO2 environment. CO2 corrosion has been recognized as one of corrosion threats resulting in a loss of pipeline integrity. In this work, the specimens taken from the X65 steel pipe were subjected to respective heat treatment which was annealing, normalizing and quenching. As-received specimens were included as control. CO2 corrosion experiments using three-electrode glass cell system were performed with and without acetic acid. Corrosion rates were measured by means of weight loss and Linear Polarization Resistance (LPR) electrochemical methods. Results showed that heat treatments were found to affect the corrosion rates. Annealed specimen showed the lowest corrosion rate while quenched specimen appeared to corrode more compared to other specimens. The presence of acetic acid in aqueous CO2 environment generally aggravated the corrosion rate for all heat-treated specimens.

In recent years, there has been an increasing interest in open-hole and filled-hole laminate failure analysis. The open and filled-hole laminate failure analysis is used in several important areas, especially in designing mechanically fastened composite joints. Various analytical, empirical, and numerical methods are available for the design of mechanically fastened composite joints. The large number of material and geometrical design variables at the preliminary design stage makes the empirical and numerical methods effortful, expensive, and time-consuming. Therefore, analytical methods are recommended over numerical and empirical methods at the preliminary design stage merely because of their simplification in calculations, making them computationally efficient. Taking this into consideration, current research presents an improvement to the analysis capabilities of the previously introduced analytical method, i.e., the coupled approach of Classical laminate theory (CLT) and Lekhnitskii solutions. These improvements include the development of failure envelops for the open-hole and filled-hole laminates, estimation of optimized filling material for attaining maximum load-bearing capacity of filled-hole laminates, and optimization of stacking sequence for maximum load-bearing capacity of open-hole and filled-hole laminates. From the failure envelop results, it was found that failure envelopes of filled-hole laminates are bigger than open-hole laminates. Furthermore, it was found that the stiffness of the filling material should be equal to the stiffness of the laminate to achieve maximum bearing strength of the filled-hole laminate. It was also demonstrated that the coupled approach of CLT and Lekhnitskii solutions may provide carpet plots that can be utilized to optimize the stacking sequence for open-hole and filled-hole laminates.