## 4 Conclusions  
This study evaluated the effects of the interaction between temperature, flow velocity, and pH on the CR (MPY) of carbon steel in a 3.5% NaCl solution. The experimental results indicated that both temperature and flow velocity had a significant effect on the CR, while the interaction between these two factors had the greatest effect. Our findings extend beyond previous research by quantitatively demonstrating that this interaction contributes the most to the MPY variance, whereas previous research typically focused on individual factor influences.  
On the other hand, pH did not have significant effect on the MPY, which is a somewhat different result compared to previous studies. While earlier research has reported significant pH influences on corrosion rates, our findings indicate that under high-salinity conditions with variable temperature and flow, the effect of pH is statistically negligible.  
Figure 10: Cumulative corrosion depth over time: comparison between predictions from the regression model, readings from the commercial LPR sensor, and measurements from test coupons.  
This suggests that corrosion management strategies in seawater cooling systems should prioritize temperature and flow control over pH adjustment.  
The comparison between the two-electrode commercial LPR sensor and the three-electrode potentiostat system revealed consistent MPY values across various factor combinations tested under the full factorial design. By incorporating multiple validation techniques, our approach offers a more robust framework than traditional single-method approaches. The MPY data were further validated through Tafel plots and immersion coupon tests, confirming the reliability of both measurement methods. The regression model, trained with 54 datasets, achieved an R\({}^{2}\) of 0.9733, indicating a high level of accuracy in explaining the variability in MPY. This represents a significant improvement over previous predictive models, especially in terms of real-time monitoring applications.  
Despite the promising results, this study has several limitations:  
1. The dataset was constrained to the factor levels and conditions tested, which may limit the model's ability to capture more complex, non-linear patterns beyond the tested ranges.
2. While short-term validation was performed, long-term monitoring in real-world marine systems remains necessary to ensure the robustness of the model over time.
3. pH variations in different environments may require further investigation to better understand their potential effects under varying salinity levels.
4. The effects of sensor contamination during extended use need more detailed investigation.  
The simulated operating condition tests further validated the model's predictive performance. These tests highlighted the potential of the LPR sensor for real-time corrosion monitoring but also revealed challenges, such as probe contamination during long-term use in marine environments. This observation highlights the importance of regular maintenance protocols and enhanced sensor designs for long-term use.  
In conclusion, this study provides valuable insights regarding the interaction between temperature, flow velocity, and pH in predicting and monitoring the corrosion rate of carbon steel in marine environments. The integrated model developed here contributes to the advancement of corrosion monitoring systems, offering a tool for improving maintenance strategies and reducing the risks of equipment degradation. By connecting laboratory findings with practical applications, our research provides a broader framework for understanding and managing corrosion in marine environments. With further research and long-term validation, the methods proposed in this study have the potential to play an important role in improving the reliability and efficiency of corrosion management in marine applications.