A mathematical model for direct ethanol fuel cells based on detailed ethanol electro-oxidation kinetics

Abstract

This paper presents an isothermal, single-phase model for direct ethanol fuel cells. The ethanol electrooxidation reaction is described using a detailed kinetic model that is able to predict anode polarization and product selectivity data. The anode kinetic model is coupled to a one-dimensional (1D) description for mass and charge transport across the membrane electrode assembly, which accounts for the mixed potential induced in the cathode catalyst layer by the crossover of ethanol and acetaldehyde. A simple 1D advection model is used to describe the spatial variation of the concentrations of the different species as well as the output and parasitic current densities along the flow channels. The proposed 1D+1D model includes two adjustable parameters that are fitted by a genetic algorithm in order to reproduce previous experimental data. The calibrated model is then used to investigate the consumption of ethanol and the production, accumulation and consumption of acetaldehyde along the flow channels, which yields the product selectivity at different channel cross-sections. A parametric study is also presented for varying ethanol feed concentrations and flow rates. The results obtained under ethanol starvation conditions highlight the role of acetaldehyde as main free intermediate, which is first produced and later consumed once ethanol is fully depleted. The detailed kinetic description of the ethanol oxidation reaction enables the computation of the four efficiencies (i.e., theoretical, voltage, faradaic, end energy utilization) that characterize the operation of direct ethanol fuel cells, thus allowing to present overall fuel efficiency vs. cell current density curves for the first time.

Overview on the Vital Step toward Addressing Platinum Catalyst Poisoning Mechanisms in Acid Media of Direct Ethanol Fuel Cells (DEFCs)

“…Currently, new catalysts are generally only assessed on the basis of their electrochemical performance (current and power vs potential), while their efficiency and emissions are neglected or only determined at ambient temperature. − There is a clear lack of information regarding ethanol electrooxidation reaction (EOR) mechanisms in DEFCs. − Therefore, more mechanistic studies should be geared to understanding how these electrode materials function in fuel cells. Cathodic and anodic materials must be optimized.…”

A Semi‐Empirical Model for Mass Transfer in Carbohydrate Polymers: A Case of Native Cassava Starch Hydration Kinetic in Hot Water Media

“…The models of concentrated parameters are advantageous for having a simple implementation since they ignore the spatial variations of the physical properties of the system and have broad applicability. [17,18] In the concentrated parameter models, it is considered that the variables of interest are influenced by only one independent variable, for example, the time or the spatial position. Due to that, the concentrated parameter models are used to describe phenomena whose spatial variation is small, even though all real phenomenon is distributed to some extent.…”

“…All real systems are distributed, however, when the spatial variation is small, it is simpler to represent the phenomena by concentrated parameter models to disregard this variation due to the complexity of distributed parameter models. [18] The models of concentrated parameters are advantageous for having a simple implementation since they ignore the spatial variations of the physical properties of the system and have broad applicability. [17,18] Few papers in the literature present the modeling and determination of model parameters to represent the mass transfer of water in starches during drying or hydration, only the papers of Njintanga and Mbofungb, [19] Kumoro et al [20] Irudayaraj and Wu, [21] and Tsukada et al [22] present these applications.…”

“…All real systems are distributed, however, when the spatial variation is small, it is simpler to represent the phenomena by concentrated parameter models to disregard this variation due to the complexity of distributed parameter models. [ 18 ] The models of concentrated parameters are advantageous for having a simple implementation since they ignore the spatial variations of the physical properties of the system and have broad applicability. [ 17,18 ]…”

“…[ 18 ] The models of concentrated parameters are advantageous for having a simple implementation since they ignore the spatial variations of the physical properties of the system and have broad applicability. [ 17,18 ]…”