The performance of a direct methanol fuel cell (DMFC) has complex nonlinear characteristics. In this paper, the performance of a DMFC has been modeled using a neural network approach. The input parameters of the DMFC model include cell geometrical and operational parameters such as the cell temperature, oxygen flow rate, channel depth of the bipolar plate, methanol concentration, cathode back pressure, and current density and the output parameter is the cell voltage. In order to predict the performance of a DMFC single cell, two types of artificial neural network (ANN) have been developed to correlate the input parameters of the DMFC to the cell voltage. The performance of the networks was investigated by varying the number of neurons, number of layers, and transfer function of the ANNs and the best one is selected based on the mean square error. The results indicated that the neural network models can predict the cell voltage with an acceptable accuracy.
Summary
Design of optimal microstructures for infiltrated solid oxide fuel cell (SOFC) electrodes is a complicated task because of the multitude of electro‐chemo‐physical phenomena taking place simultaneously that directly affect working conditions (cell temperature, current density, and flow rates) of the SOFC electrode and therefore its performance. In this study, an innovative design paradigm is presented to obtain a part of geometry‐related electrochemical and physical properties of an infiltrated SOFC electrode. A range of digitally realized microstructures with different backbone porosity and electrocatalyst particle loading under various deposition conditions are generated. Triple phase boundary (TPB), active surface density of particles, and gas transport factor are evaluated in realized models on the basis of selected infiltration strategy. On the basis of this database, a neural network is trained to relate desired range of input geometric parameters to a property hull. The effect of backbone porosity, loading, distribution, and aggregation behavior of particles is systematically investigated on the performance indicators. It is shown that from the microstructures with very high amount of TPB and particle contact surface density, a relatively low gas diffusion factor should be expected; meanwhile, increasing those parameters does not have sensible contradiction with each other. Excessive agglomerating of particles has a negative effect on TPB density, but the distribution of seeds always has a positive effect. Direct search and genetic algorithm optimization techniques are used finally to achieve optimal microstructures on the basis of assumed target functions for effective geometric properties.
Solid oxide fuel cell electrodes with directional properties have shown their potential to get the maximum electrochemical reaction sites, gas diffusivity and ionic conductivity, simultaneously. New manufacturing methods, like freeze type casting, have used to make this kind on electrodes. In this work, the effect of backbone directional behavior in infiltrated solid oxide fuel cell (SOFC) was simulated. A series of directional backbones were generated by a statistical method and analyzed in regard of available active surface density and phase tortuosity. Different amount of electrocatalyst particles virtually deposited on the surface of those scaffolds. Some geometric parameters like triple phase boundary (TPB) density, active surface density of particles and the pore tortuosity were extracted from those realized models. The simulations showed that the optimum amount of infiltration to get the maximum TPB density or active surface density of impregnated particles can be varied depend on the porosity and geometric anisotropy of scaffolds. Being directional in backbones, normal to the electrolyte, has a positive effect on active electrochemical sites especially active surface density of deposited particles. Also it can improve the gas transport even in low porosity microstructures, but adding electrocatalyst particles may increase the pore tortuosity considerably. Accordingly, directional backbones have the potential of increasing the performance of infiltrated electrodes via adding electrochemical sites and gas diffusivity.
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