## 1 Introduction  
When dealing with industrial environments, acidic corrosion often becomes a threat of primary importance that needs to be carefully taken into account [1, 2]. The environments and the causes that lead to the acid production and consequent corrosion of metal structures can vary and differ significantly, ranging from offshore structures affected by acid producing bacteria [3, 4, 5] to petrochemical plants influenced by weak acids corrosion [6, 7]. Indeed, one of the most worldly known environments affected by this kind of corrosion is the oil and gas industry, which has to deal with the problem of sweet and sour corrosion caused by the presence of carbon dioxide and hydrogen sulfide, respectively [8, 9, 10]. Nonetheless, these are not the only corrosive species that threaten oilfield pipelines: a variety of weak acids, acetic acid on top of them [11], can be present in the aqueous phase in contact with the internal pipe walls [12, 13, 14, 15], reaching concentrations as high as hundreds of milligrams per liter [16, 11, 17]. The speciation and electrochemical behavior of weak acids, that are still partially debated in the literature, can create a localized condition at the metal surface that can be different from the bulk solution, sometimes resulting in a corrosive environment that is more dangerous to metal structures with respect to the one created by strong acids. In this context, the study and prediction of the acidic corrosion process becomes a fundamental step to guarantee the proper durability of the metal structure [18, 19, 20, 21, 22, 23] and to avoid the severe implications, including health and safety issues, that can derive from its inadequate management [1, 24, 25, 26, 27, 28, 29, 30]. In recent decades, the predictive modeling of acidic corrosion hasmainly focused on the sweet corrosion effect that deeply affects the petrochemical plants. Most of these models are characterized by their empirical [31; 32; 33] or semi-empirical [34; 35; 36; 37; 38] foundations, fully or partially basing their estimations on experimental databases. These kinds of models have proven to be very helpful in practical scenarios; however, their range of applicability is limited to the empirical conditions used to formulate them. Mechanistic models [39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49], on the other end, do not base their predictions on experimental data: their foundations are completely theoretical, which allows them to have a wider range of applicability. In this context, a new model was developed with the goal of being a transversal tool, characterized by a mechanistic nature, applicable not only to sweet corrosion, but also to any other kind of acidic corrosion. In particular, the theoretical approach utilized in this work is called the \"Tafel-Piontelli model\", a tool originally presented by [PERSON] [1; 50] and then mechanistically developed throughout the years [51; 52; 53; 54]; here it is used to predict the corrosion rate of carbon steel exposed to weakly acidic electrolytes. In this work, the approach defined for weak acids in [54] is validated in valeric and acetic acid solutions. Valeric acid is present in certain process streams and effluents within the polymer industry and it serves as an intermediate in several industrial processes for the production of solvents, perfumes, softeners, and fungicides [55]. Acetic acid is a very common acid in industrial environments (above all in the petrochemical industry), extensively studied in the literature, as it has been shown to influence the corrosion process. The validation tests are performed in acidic aqueous solutions with pH ranging from 3 to 4 and temperature ranging from 20 \({}^{\circ}\)C to 60 \({}^{\circ}\)C. The experimental results of the electrochemical tests and weight loss tests are compared to the predictions of the Tafel-Piontelli model, allowing us to evaluate its performance and further expanding its applicability limits.