RESUMO
In polymer membrane fuel cells (PEMFC), the pore microstructure and the effective diffusion coefficient ( D eff ) of the catalytic layer have a significant impact on the overall performance of the fuel cell. In this work, numerical methods to simulate PEMFC catalytic layers were used to study the effect of isotropy ( I xy ) on the D eff . The proposed methodology studies reconstructed systems by Simulated Annealing imaging with different surface fractions of microstructures composed by two diffusive phases: agglomerates and pores. The D eff is determined numerically by the Finite Volume Method solved for Fick's First Law of Diffusion. The results show that the proposed methodology can effectively quantify the effect of isotropy on the D eff for both diffusion phases. Two trends were obtained in the magnitude of the D eff concerning the change in isotropy: (1) an analytical equation is proposed in this article for D eff ≥ 5 % D 0 and (2) numerical solutions are determined for D eff < 5 % D 0 . In our analytical equation are both a lineal and a logarithmic sweep. When the surface fraction is ∅ = 50%, the D eff decreases more linearly than ∅ = 10 % at the beginning of the isotropy change, which indicates that small changes in isotropy in the particulate material modify it drastically; under these conditions the diffusion coefficient in the pore is predominant. (3) When the surface fraction is less than 50%, the D eff decreases more exponentially at the beginning and more linearly at the end of the isotropy change, which shows that small isotropy changes in the bar-aligned material drastically alter it. In this trend, diffusion in the agglomerate is less affected by isotropy. The proposed methodology can be used as a design tool to improve the mass transport in porous PEMFC electrodes.
RESUMO
Zero-export photovoltaic systems are an option to transition to Smart Grids. They decarbonize the sector without affecting third parties. This paper proposes the analysis of a zero-export PVS with a green hydrogen generation and storage system. This configuration is feasible to apply by any self-generation entity; it allows the user to increase their resilience and independence from the electrical network. The technical issue is simplified because the grid supplies no power. The main challenge is finding an economic balance between the savings in electricity billing, proportional to the local electricity rate, and the complete system's investment, operation, and maintenance expenses. This manuscript presents the effects of the power sizing on the efficacy of economic savings in billing (ηSaving) and the effects of the cost reduction on the levelized cost of energy (LCOE) and a discounted payback period (DPP) based on net present value. In addition, this study established an analytical relationship between LCOE and DPP. The designed methodology proposes to size and selects systems to use and store green hydrogen from the zero-export photovoltaic system. The input data in the case study are obtained experimentally from the Autonomous University of the State of Quintana Roo, located on Mexico's southern border. The maximum power of the load is LPmax = 500 kW, and the average power is LPmean = 250 kW; the tariff of the electricity network operator has hourly conditions for a medium voltage demand. A suggested semi-empirical equation allows for determining the efficiency of the fuel cell and electrolyzer as a function of the local operating conditions and the nominal power of the components. The analytical strategy, the energy balance equations, and the identity functions that delimit the operating conditions are detailed to be generalized to other case studies. The results are obtained by a computer code programmed in C++ language. According to our boundary conditions, results show no significant savings generated by the installation of the hydrogen system when the zero-export photovoltaic system Power ≤ LPmax and DPP ≤ 20 years is possible only with LCOE ≤ 0.1 $/kWh. Specifically for the Mexico University case study, zero-export photovoltaic system cost must be less than 310 $/kW, fuel cell cost less than 395 $/kW, and electrolyzer cost less than 460 $/kW.
RESUMO
The study of the microstructure of random heterogeneous materials, related to an electrochemical device, is relevant because their effective macroscopic properties, e.g., electrical or proton conductivity, are a function of their effective transport coefficients (ETC). The magnitude of ETC depends on the distribution and properties of the material phase. In this work, an algorithm is developed to generate stochastic two-phase (binary) image configurations with multiple geometries and polydispersed particle sizes. The recognizable geometry in the images is represented by the white phase dispersed and characterized by statistical descriptors (two-point and line-path correlation functions). Percolation is obtained for the geometries by identifying an infinite cluster to guarantee the connection between the edges of the microstructures. Finally, the finite volume method is used to determine the ETC. Agglomerate phase results show that the geometry with the highest local current distribution is the triangular geometry. In the matrix phase, the most significant results are obtained by circular geometry, while the lowest is obtained by the 3-sided polygon. The proposed methodology allows to establish criteria based on percolation and surface fraction to assure effective electrical conduction according to their geometric distribution; results provide an insight for the microstructure development with high projection to be used to improve the electrode of a Membrane Electrode Assembly (MEA).
RESUMO
Electrochemical electrodes comprise multiple phenomena at different scales. Several works have tried to model such phenomena using statistical techniques. This paper proposes a novel process to work with reduced size images to reconstruct microstructures with the Simulated Annealing method. Later, using the Finite Volume Method, it is verified the effect of the image resolution on the effective transport coefficient (ETC). The method can be applied to synthetic images or images from the Scanning Electron Microscope. The first stage consists of obtaining the image of minimum size, which contains at least 98% of the statistical information of the original image, allowing an equivalent statistical study. The image size reduction was made by applying an iterative decimation over the image using the normalized coarseness to compare the amount of information contained at each step. Representative improvements, especially in processing time, are achieved by reducing the size of the reconstructed microstructures without affecting their statistical behavior. The process ends computing the conduction efficiency from the microstructures. The simulation results, obtained from two kinds of images from different materials, demonstrate the effectivity of the proposed approach. It is important to remark that the controlled decimation allows a reduction of the processor and memory use during the reconstruction and ETC computation of electrodes.