Kwang-Hu Jung

This study applies statistical approaches utilizing linear regression and artificial neural networks (ANNs) to predict the corrosion behavior of austenitic stainless steels (316L, 904L, and AL-6XN) under various environmental conditions. The environmental variables considered include temperature (30–90 °C), chloride ion concentration (20–40 g/L), and pH (2–6). Analysis of variance (ANOVA) confirmed that the input variables, including the Pitting Resistance Equivalent Number (PREN ranging from 24 to 45), significantly affect the critical pitting potential. The influence of the variables was ranked in the order: PREN, temperature, pH, and chloride ion concentration. A linear regression model was developed using significant factors and interactions identified at the 95% confidence level, achieving a predictive performance with R² = 0.789 for critical pitting potential. To predict potentiodynamic polarization curves, an ANN based on supervised learning with backpropagation was employed. The ANN model demonstrated a remarkably high predictive performance with R² = 0.972 in complex corrosion environments. The predicted polarization curves reliably estimated electrochemical characteristics such as corrosion current, corrosion potential, and pitting potential. These results provide a valuable tool for predicting and understanding the corrosion behavior of stainless steels, which can aid in corrosion prevention strategies and material selection decisions.

This study aims to evaluate the corrosion behavior of ductile cast iron (FCD-500) under simulated marine engine operating conditions and to determine the optimal corrosion inhibitor conditions under chloride contamination. Experiments were conducted at 50 °C and 80 °C, with different Cl⁻ concentrations (0–500 ppm) and sodium nitrite (NaNO₂)-based inhibitor concentrations (0; 9000; 15,000; 17,000 ppm). Immersion and electrochemical tests were performed to analyze the microstructural corrosion characteristics and inhibitor efficiency. The results indicated that as the Cl⁻ concentration increased, proportionally higher levels of inhibitor were required to maintain surface stability. The maximum inhibition efficiency, approximately 97.3%, was achieved with an inhibitor concentration of 17,000 ppm, at a Cl⁻ concentration of 100 ppm.

In this study, a model was proposed to predict the corrosion rate (Mils per Year, MPY) of carbon steel in a 3.5% NaCl solution, with the objective of comparing the effectiveness of a commercial LPR sensor against traditional electrochemical methods, using potentiostat-based LPR techniques. The primary factors considered in the experiments were temperature, flow velocity, and pH, tested through a full factorial design to identify the most influential variables. Statistical analysis showed that temperature and flow velocity had a significant effect on corrosion rate, with their interaction having the most substantial impact. In contrast, pH had no statistically significant influence within the tested conditions, likely due to the dominant effects of temperature and flow velocity in the high-salinity environment. The MPY data were validated through Tafel plots, immersion coupon tests, and other electrochemical techniques to confirm the reliability of the measurements. A regression model trained on 54 MPY data points demonstrated high accuracy, achieving a coefficient of determination (R²) of 0.9733. The model also provided reliable predictions for factor combinations excluded from the training dataset. Additionally, scenario-based evaluations highlighted the model’s performance under simulated operating conditions, while revealing challenges related to sensor contamination during long-term use. These findings emphasize the potential of commercial LPR sensors as effective tools for real-time corrosion monitoring and demonstrate the utility of the regression model in marine environments.

In this study, the optimization of the operational parameters for a single proton exchange membrane fuel cell (PEMFC) was carried out using the Taguchi method and orthogonal array. The operating parameters were H₂ stoichiometry, air stoichiometry, cell temperature, and back pressure of the anode∙cathode, each with three levels. The performance of the PEMFC, operated according to the L₉ orthogonal arrangement, was evaluated through I–V curves at a step-up current loading ranging from 0.1 to 0.7 A/cm². The results indicated that the anode∙cathode back pressure had the greatest sensitivity to the output voltage compared to the other operating parameters. Increasing the back pressure resulted in higher current output densities at higher values than those applied in the orthogonal arrangement. As the back pressure increased, the output voltage tended to increase at each current density. However, for operating conditions above 150 kPa, the improvement in cell performance was either not significant or tended to decrease. Therefore, it can be concluded that the Taguchi method and orthogonal array are effective tools for selecting the optimal operating conditions for PEMFC.