Swarm Intelligence-Based Methodology for Scanning Electron Microscope Image Segmentation of Solid Oxide Fuel Cell Anode

Segmentation of images from scanning electron microscope, especially multiphase, poses a drawback in their microstructure quantification process. The labeling process must be automatized due to the time consumption and irreproducibility of the manual labeling procedure. Here we show a swarm intelligence-driven filtration methodology performed on raw solid oxide fuel cell anode’s material images to improve the segmentation methods’ performance. The methodology focused on two significant parts of the segmentation process, which are filtering and labeling. During the first one, the images underwent filtering by applying a series of filters, whose operation parameters were determined using Particle Swarm Optimization upon a dedicated cost function. Next, Seeded Region Growing, k-Means Clustering, Multithresholding, and Simple Linear Iterative Clustering Superpixel algorithms were utilized to label the filtered images’ regions into consecutive phases in the microstructure. The improvement was presented for three different metrics: the Misclassification Ratio, Structural Similarity Index Measure, and Mean Squared Error. The obtained distribution of metrics’ performances was based on 200 images, with and without filtering. Results indicate an improvement up to 29%, depending on the metric and method used. The presented work contributes to the ongoing efforts to automatize segmentation processes fully for an increasing number of tomographic measurements, particularly in solid oxide fuel cell research

Swarm Intelligence-Based Methodology for Scanning Electron Microscope Image Segmentation of Solid Oxide Fuel Cell Anode

Segmentation of images from scanning electron microscope, especially multiphase, poses a drawback in their microstructure quantification process. The labeling process must be automatized due to the time consumption and irreproducibility of the manual labeling procedure. Here we show a swarm intelligence-driven filtration methodology performed on raw solid oxide fuel cell anode’s material images to improve the segmentation methods’ performance. The methodology focused on two significant parts of the segmentation process, which are filtering and labeling. During the first one, the images underwent filtering by applying a series of filters, whose operation parameters were determined using Particle Swarm Optimization upon a dedicated cost function. Next, Seeded Region Growing, k-Means Clustering, Multithresholding, and Simple Linear Iterative Clustering Superpixel algorithms were utilized to label the filtered images’ regions into consecutive phases in the microstructure. The improvement was presented for three different metrics: the Misclassification Ratio, Structural Similarity Index Measure, and Mean Squared Error. The obtained distribution of metrics’ performances was based on 200 images, with and without filtering. Results indicate an improvement up to 29%, depending on the metric and method used. The presented work contributes to the ongoing efforts to automatize segmentation processes fully for an increasing number of tomographic measurements, particularly in solid oxide fuel cell research.</jats:p

Transport Phenomena in a Banded Solid Oxide Fuel Cell Stack&mdash;Part 1: Model and Validation

This paper primarily focuses on the formulation and validation of mathematical and numerical models for a new electrolyte-supported solid oxide fuel cell stack. By leveraging numerical modeling, the main goal is to deepen the understanding of the operational aspects and transport phenomena within this system. The developed models are implemented in ANSYS, Inc., Fluent software, which enables a range of simulations. To validate the models, a stack fabrication methodology, a prototype construction, and conducted electrochemical tests were proposed. The simulated current-voltage characteristics for two different operating temperatures and three different fuel compositions were compared with the experimental measurements with satisfactory agreement. The counter-flow configuration was simulated and compared to the co-flow arrangement. The numerical simulation has demonstrated its efficacy in identifying possible design imperfections and enhancing the operational conditions of the prototype stack. Moreover, the developed model was further used, in Part 2 of this paper, to analyze the improvement options implementation for the next stage of the prototype