Mathematical Modeling of Alkaline Direct Glycerol Fuel Cells

Alessandra Pezzini, Ubiranilson João de Castro, Deborah S. B. L. de Oliveira, Germano Tremiliosi-Filho and Ruy de Sousa Júnior ()
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Alessandra Pezzini: Graduate Program in Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos 13565-905, SP, Brazil
Ubiranilson João de Castro: Graduate Program in Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos 13565-905, SP, Brazil
Deborah S. B. L. de Oliveira: Faculty of Engineering and Physical Sciences, University of Leeds, Leeds LS2 9JT, UK
Germano Tremiliosi-Filho: São Carlos Institute of Chemistry, University of São Paulo, Avenida Trabalhador São-carlense, 400, São Carlos 13566-590, SP, Brazil
Ruy de Sousa Júnior: Graduate Program in Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos 13565-905, SP, Brazil

Energies, 2023, vol. 16, issue 19, 1-30

Abstract: Mathematical modeling and computer simulation are tools of great importance for the development of fuel cells. Thus, the objective of this work is to carry out the kinetic modeling of glycerol oxidation in a DGFC (direct glycerol fuel cell), considering two different approaches: (1) realistic phenomenological models for the partial oxidation of glycerol in Pt/C, considering its adsorbed intermediates; (2) models of artificial neural networks (ANN—artificial neural networks) for oxidation mainly of PtAg/C and PtAg/MnO x /C. The models were fitted to experimental data already available for validation and determination of their parameters, both using Matlab software, v. R2018a (MathWorks, Natick, MA, USA). Results for the phenomenological models developed showed excellent fits for the polarization curve, with an RMSE (root mean squared error) value on the order of 0.352 to 0.404 mA/cm 2 , in addition to coverage fractions consistent with the literature for the adsorbed species. The kinetic parameters with the greatest influence on the response of the models were those associated with the consumption of glyceric acid and the formation of tartronic acid and with the dissociative adsorption of water and the formation of Pt-O ads active sites. Regarding the neural models, excellent prediction fits were obtained for all of them, with RMSE values on the order of 0.008 to 0.014 mA/cm 2 , indicating the possibility of representing the functional interdependence between input variables and the density cell current for cases where it would be too complex to do so via mechanistic modeling (i.e., for PtAg/C and PtAg/MnO x /C oxidation).

Keywords: direct glycerol fuel cells; mechanistic modeling; artificial neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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