Experimental study on predicting the corrosion behavior of carbon steel in various corrosive environments using artificial neural networks
1
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
In this study, potentiodynamic polarization curves of carbon steel were employed to systematically characterize and predict its electrochemical corrosion behavior under varying environmental conditions. A three-factor full factorial design was utilized to vary critical parameters such as temperature, pH, and salinity, simulating diverse marine conditions. Using the TensorFlow 2.0 framework, a model based on an Artificial Neural Network (ANN) was constructed to predict the corrosion current density, a key indicator of corrosion rate. The ANN model demonstrated remarkable agreement with experimental data, achieving a correlation coefficient ex-ceeding 0.98 for the training dataset. However, when extrapolating to conditions outside the training data, the model exhibited dimin-ished accuracy. This emphasizes the potential of using ANN for corrosion prediction and underscores the importance of iterative model optimization in response to comprehensive datasets.
Abstract
In this study, potentiodynamic polarization curves of carbon steel were employed to systematically characterize and predict its electrochemical corrosion behavior under varying environmental conditions. A three-factor full factorial design was utilized to vary critical parameters such as temperature, pH, and salinity, simulating diverse marine conditions. Using the TensorFlow 2.0 framework, a model based on an Artificial Neural Network (ANN) was constructed to predict the corrosion current density, a key indicator of corrosion rate. The ANN model demonstrated remarkable agreement with experimental data, achieving a correlation coefficient ex-ceeding 0.98 for the training dataset. However, when extrapolating to conditions outside the training data, the model exhibited dimin-ished accuracy. This emphasizes the potential of using ANN for corrosion prediction and underscores the importance of iterative model optimization in response to comprehensive datasets.
참고문헌 (14)
- X. Hou, "Corrosion and protec-tion of metal in the seawater desalination", IOP Conference Series: Earth and Environmental Science, 108(2), pp.022037~ (2018)
- X. Jiang, "Data-driven pitting evolution prediction for corrosion-resistant alloys by time-series anal-ysis", npj Materials Degradation, 6(1), pp.92~ (2022)[DOI]
- C. Wang, "Study of electrochemical corrosion on Q235A steel under stray cur-rent excitation using combined analysis by electrochemical impedance spectroscopy and artificial neural network", Construction and Building Materials, 247, pp.118562~ (2020)[DOI]
- Y. Liu, "Prediction of corrosion rates of Ni-TiN composite coating using a radial basis func-tion neural network", International Journal of Electrochem-ical Science, 17(6), pp.220626~ (2022)[DOI]
- 박주용, "플럭스코어드와이어의 용접특성 예측을 위한 탄력적 오류역전파 신경망 모델에 관한 연구", 한국마린엔지니어링학회지, 42(7), pp.531~538 (2018), 한국마린엔지니어링학회[DOI]
- A. A. Dastgerdi, "Experimental design to study the influence of temperature, pH, and chloride concentration on the pit-ting and crevice corrosion of UNS S30403 stainless steel", Corrosion Science, 159, pp.108160~ (2019)[DOI]
- A. H. Alamri, "Localized corrosion and mitigation approach of steel materials used in oil and gas pipelines–An over-view", Engineering Failure Analysis, 116, pp.104735~ (2020)[DOI]
- F. Arjmand, "Influence of pH and chloride concentration on the corrosion behavior of unalloyed copper in NaCl solution: A comparative study between the micro and macro scales", Materials, 5(12), pp.2439~2464 (2012)[DOI]
- C. G. Soares, "Influ-ence of environmental factors on corrosion of ship struc-tures in marine atmosphere", Corrosion Science, 51(9), pp.2014~2026 (2009)[DOI]
- M. T. Gudze, "Operational based corro-sion analysis in naval ships", Corrosion science, 50(12), pp.3296~3307 (2008)[DOI]
- A. Heyer, "Ship ballast tanks a review from microbial corrosion and electrochemical point of view", Ocean Engineering, 70, pp.188~200 (2013)[DOI]
- D. A. Jones, "qPrinciples and Prevention of Corrosion" (1996), Prentice Hall
- X. L. Zhang, "Effects of scan rate on the potentiodynamic polarization curve obtained to determine the Tafel slopes and corrosion current density", Corrosion Science, 51(3), pp.581~587 (2009)[DOI]
- A. H. Jiang, "Accelerating deep learning by focusing on the biggest losers", ~ (2019)