## 3 Results and Discussion
### Corrosion Rate Estimation

Table 8: Statistical analysis of experimental and predicted dependence of corrosion rate (CR) [mm/y] on temperature for valeric and acetic acids. Figure 9: Comparison between experimental and predicted corrosion rate exponential decay with respect to temperature in valeric acid solutions (**a**) and acetic acid solutions (**b**) with pH ranging from 3.0 to 4.0. In the context of valeric acid solutions (Figure 10), the experimental and predicted regression lines are very close, almost overlapping at pH 4 at 20 \({}^{\circ}\)C and 60 \({}^{\circ}\)C.  
These results suggest that the Tafel-Piontelli model can capture the nuanced corrosive behavior of weak acids and its interaction with metal surfaces. In the case of acetic acid solutions (Figure 11), the model offers estimates that are close to the experimental results, particularly at lower temperatures. Indeed, the experimental and estimated data overlap almost completely at 20 \({}^{\circ}\)C in the entire considered pH range and have parallel and very close trends at 40 \({}^{\circ}\)C, almost overlapping at higher pH values. At 60 \({}^{\circ}\)C, the model excellently captures the corrosive behavior at pH 4, while it tends to underestimate the corrosion rate with the increase in the acidity of the electrolyte. The model's effectiveness in representing the dependence of the corrosion rate on the solution's pH is assessed by comparing the mean slopes of the regression lines obtained for the theoretical and experimental corrosion rate values, as well as the 95% confidence intervals, as shown in Table 9. This quantitative assessment corroborates the findings shown qualitatively in Figure 10, where the results for valeric acid indicate that the theoretical and experimental lines are almost parallel at all temperatures considered. In contrast, the results for acetic acid, similar to the qualitative assessment of the curves presented in Figure 11, show that the model accurately predicted the dependence on pH at 20 \({}^{\circ}\)C and 40 \({}^{\circ}\)C but grossly misrepresented it at 60 \({}^{\circ}\)C. At this higher temperature, the model and experimental data align for high pH values but underestimate the corrosion rate for pH values below 3.5. This misalignment indicates that a reevaluation of the model's temperature sensitivity may be needed to better capture the experimental evidence. Figure 11: Comparison between experimental and predicted corrosion rate exponential decay with respect to pH in acetic acid solutions at 20 \({}^{\circ}\)C (**a**), 40 \({}^{\circ}\)C (**b**), and 60 \({}^{\circ}\)C (**c**). Figure 10: Comparison between experimental and predicted corrosion rate exponential decay with respect to pH in valeric acid solutions at 20 \({}^{\circ}\)C (**a**), 40 \({}^{\circ}\)C (**b**), and 60 \({}^{\circ}\)C (**c**). The findings presented allowed us to evaluate the behavior of carbon steel when exposed to weakly acidic environments, which is typical of several industrial applications. The loss of metal weight due to corrosion is a serious threat in a variety of industries and infrastructures, and it is the main cause of failures when dealing with oil transportation pipelines. In these cases, the application of protective measures (i.e., protective coatings and cathodic protection techniques) needs to be coupled with consistent monitoring, a proper design of the structure at the beginning of its life, and a prediction tool for the evaluation of the corrosion behavior at the interface between the acidic electrolyte and the metal structure, which will ensure safe operational conditions.