Performance improvement of solid oxide fuel cells by combining three-dimensional CFD modeling, artificial neural network and genetic algorithm

• Published in: Energy conversion and management Vol. 268; p. 116026
• Main Authors: Xu, Guoping, Yu, Zeting, Xia, Lei, Wang, Changjiang, Ji, Shaobo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.09.2022
• Subjects: administrative management; Artificial neural network; commercialization; Computational fluid dynamic; electric power; electrochemistry; electrodes; energy conversion; fuel cells; Genetic algorithm; geometry; mass transfer; neural networks; Optimization; power generation; prediction; Solid oxide fuel cell; support vector machines; system optimization; Computational fluid dynamic; Artificial neural network; Genetic algorithm; Solid oxide fuel cell; Optimization
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2022.116026

•A three-dimensional CFD model aiming to improve SOFC’s performance was developed.•Nineteen artificial intelligence algorithms were examined and used to train the database.•Genetic algorithm was employed to optimize the power output of SOFC. Solid oxide fuel cell (SOFC) is the electrochemical device that directly convert the chemical energy of fuels into electrical energy, which are considered one of the promising methods for achieving high power generation efficiency. However, the commercialization of SOFC encounters the challenge due to its high manufacturing and operating cost. This study aims to present a framework and methodology for improving SOFC’ performance assisted by computational fluid dynamic (CFD) modeling, artificial neural network (ANN), and genetic algorithm (GA). Firstly, a three-dimensional computational fluid dynamic (CFD) model, referring to three types of parameters, e.g. geometry parameters, microscopic parameters and operating conditions, was developed and then the databases were obtained. Then 19 widely used intelligence algorithms, e.g. Artificial Neural Network (ANN), Boltzmann Machines (BMs), Support Vector Machines (SVMs), etc., were employed to train the databases. Next, the developed ANN surrogate model was used to replace the complicated and time-consuming CFD model and to predict SOFC’s performance and optimize the power density output of SOFC. Finally, the system optimization was performed by using genetic algorithm (GA) to maximize the power density. The results showed that artificial neural network (ANN) achieved the best accuracy (R2 = 0.99889) in terms of predictions of SOFC performance. Besides, it was found that the optimal SOFC had a better gas concentration distribution which can enhance the mass transfer in the electrode, and thus the SOFC performance was improved. The combination of CFD modeling, ANN and GA can provide a promising solution for the performance prediction, improvement and optimization of SOFC accurately and rapidly.