Performance Analysis of Anode-Supported Solid Oxide Fuel Cells: A Machine Learning Approach

Abstract

Prior to the long-term utilization of solid oxide fuel cell (SOFC), one of the most remarkable electrochemical energy conversion devices, a variety of difficult experimental validation procedures is required, so it would be time-consuming and steep to predict the applicability of these devices in the future. For numerous years, extensive efforts have been made to develop mathematical models to predict the effects of various characteristics of solid oxide fuel cells (SOFCs) components on their performance (e.g., voltage). Taking advantage of the machine learning (ML) method, however, some issues caused by assumptions and calculation costs in mathematical modeling could be alleviated. This paper presents a machine learning approach to predict the anode-supported SOFCs performance as one of the most promising types of SOFCs based on architectural and operational variables. Accordingly, a dataset was collected from a study about the effects of cell parameters on the output voltage of a Ni-YSZ anode-supported cell. Convolutional machine learning models and multilayer perceptron neural networks were implemented to predict the current-voltage dependency. The resulting neural network model could properly predict, with more than 0.998 R2 score, a mean squared error of 9.6 10−5, and mean absolute error of 6 10−3 (V). Conventional models such as the Gaussian process as one of the most powerful models exhibits a prediction accuracy of 0.996 R2 score, 10−4 mean squared, and 6 10−3 (V) absolute error. The results showed that the built neural network could predict the effect of cell parameters on current-voltage dependency more accurately than previous mathematical and artificial neural network models. It is noteworthy that this procedure used in this study is general and can be easily applied to other materials datasets.

Machine learning-guided morphological property prediction of 2D electrospun scaffolds: the effect of polymer chemical composition and processing parameters

“…This method of analysis can potentially be the best approach with the lowest error for prediction of the physical properties of electrospun scaffolds to save time, cost, and material. 30 Machine learning is typically divided into two main categories: (1) shallow learning; and (2) deep learning. [31][32][33] Deep learning uses many successive layered representations of data (i.e., hundreds of convolutions or lters), while shallow learning typically uses one or two layered representations of the data.…”

“…This method of analysis can potentially be the best approach with the lowest error for prediction of the physical properties of electrospun scaffolds to save time, cost, and material. 30…”