|Support type:||Scholarships in Brazil - Scientific Initiation|
|Effective date (Start):||August 01, 2018|
|Effective date (End):||July 31, 2019|
|Field of knowledge:||Engineering - Chemical Engineering - Chemical Technology|
|Principal Investigator:||Ruy de Sousa Júnior|
|Grantee:||Diogo de Aguiar Alves|
|Home Institution:||Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil|
Direct alcohol fuel cells are a subcategory of proton exchange membrane cells. They have been gaining the attention of researchers because of their low operating temperature, solid state of the electrolyte and good efficiency compared to other types of fuel cells. In this context, this work aims to model and simulate the kinetics of direct ethanol fuel cells by means of phenomenological and empirical models. Firstly, ideal models based on Tafel kinetics will be considered, representing the complete oxidation of ethanol (for Pt-Sn anodes with different levels of Sn). Subsequently, realistic models will be considered, taking into account the formation of by-products and lower generation of electrons. This type of model will describe the catalytic oxidation of ethanol by adsorption of species and formation of partially oxidized products. For Pt3Sn anodes submitted to different thermal treatments, an empirical model based on an artificial neural network will be developed to map the potential / current relationship as a function of the treatment temperature. The modeling of the kinetics involved in the ethanol oxidation process is fundamental for the study and development of more efficient direct ethanol fuel cells. In addition, the use of empirical models may also be interesting. A neural network that can map the current / potential relationship with good precision will show that this approach can be used in fuel cell modeling.