## 4 Conclusions  
We arranged 25 orthogonal experiment plans, using face-centered cubic designs derived from the response surface method to improve the shortcomings of existing oil and gas pipeline corrosion prediction models. Corrosion experiments with high-temperature and high-pressure reactors were performed according to experimental arrangements. The corrosion depths of the coupons under different working conditions were obtained. We developed a new corrosion failure prediction model for carbon steel oil and gas pipelines, using the least squares method on corrosion experiment data. This model introduced two variables: time and flow rate. The determination coefficient, \(R^{2}\), of the model was 0.9573, which illustrates a good fitting effect. Three machine learning algorithm prediction models, namely the BP neural network, random forest regression, and optimized neural network, were constructed and compared with the new prediction model proposed in this paper. The prediction model results described in this paper are consistent with the prediction trend of the machine learning prediction models, which had high accuracy, verifying the feasibility and accuracy of the proposed prediction model. Finally, based on the constructed prediction model, we developed a corrosion failure prediction management system for carbon steel oil and gas pipelines, making corrosion prediction for submarine oil and gas transport pipelines more convenient and faster. Based on the uniform corrosion failure prediction model in this paper, the pitting corrosion failure prediction model of the stainless-steel pipeline for oil and gas transmission can be studied further.  
Conceptualization, J.C.; methodology, Z.L.; software, Z.L.; validation, Y.W. and Z.L.; formal analysis, Y.W.; investigation, J.C.; resources, W.X.; data curation, Y.W.; writing--original draft preparation, Y.W. and Z.L.; writing--review and editing, J.C.; visualization, Y.W.; supervision, W.X. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Corrosion Condition** & **Mild** & **Moderate** & **Severe** & **Extremely Severe** & **Perforation** \\ \hline Maximum depth of & \(<\)1 & 1–2 & 2–50\% & 50–80\% & \(>\)80\% \\ corrosion pit/mm & & & Wall thickness & Wall thickness & Wall thickness \\ \hline \end{tabular}
\end{table}
Table 3: Corrosion failure criteria for carbon steel oil and gas pipelines.  
**Funding:** This research was funded by the high-tech ship research project of the Ministry of Industry and Information Technology of the People's Republic of China (grant no. 2018 GXB01-02-003), the Strategic Research and Consulting project of the Chinese Academy of Engineering (grant no. 2022-DFZD-36), and the Fundamental Research Funds for the Central Universities (grant no. 22 CX01003A-1).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
**Acknowledgments:** We sincerely acknowledge [PERSON] and [PERSON] for their administrative and technical support and guidance.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Offshore Oil Engineering (Qingdao) Co., Ltd. The remaining authors declare that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.