Neurofuzzy modelling on the influence of Pt–Sn catalyst properties in direct ethanol fuel cells performance: Fuzzy inference system generation and cell power density optimization.

Pt-based materials are commonly employed as catalysts in the electrooxidation of ethanol in direct ethanol fuel cells (DEFC). However, due to the complex nature of electrooxidation, the effect of catalyst properties on fuel cell power generation is not easily described by mechanistic models. Fuzzy systems can be used instead precisely for their ability to provide a more direct and qualitative/semiquantitative cause-effect relationship. In this study, a neurofuzzy model was developed to relate the effect of 5 input variables (4 catalyst properties, namely, crystal size, surface area, presence of PtSn phase and Pt L 3 -edge whiteline integrated intensity, and the cell potential) on cell current density. The fuzzy inference system (FIS) structure was constructed in MATLAB with ANFIS (Adaptive network-based FIS) based on experimental data used for training and validation. A 4-variable FIS (not including integrated intensity) was initially created and used as a basis for developing the 5-variable FIS, thus addressing the issue of fewer experimental data points available for integrated intensity. Both FIS structures exhibited very good fits to experimental data. The addition of integrated intensity as an input variable did not affect the fitted fuzzy model parameters for the other input variables and, therefore, a broader and more relevant model that included an electronic property of the catalyst was created. Response surface analyses, corroborated by particle swarm optimization, indicated that decreasing crystal size has the greatest effect in maximizing power density. Medium potentials and medium integrated intensity are also favorable. In addition, presence of the PtSn phase in moderate amounts is not unfavorable. With these values it was possible to predict the optimization of the power density value to 24.3 mW/cm2, compared to the best experimental value of 19.6 mW/cm2. • Neurofuzzy DEFC model to relate catalyst properties and potential to cell power. • FIS structures exhibited very good fits to experimental data. • Reducing crystal size has the greatest effect on maximizing power generation. • Medium potential and integrated intensity were favorable to higher power density. • PSO predicted optimal power density of 24.3 mW/cm2. [ABSTRACT FROM AUTHOR]