MODELLING OF SOFC STACK TEMPERATURE FIELD BASED ON DEEP NEURAL NETWORK

被引：0
|
作者：
Wu X. [1 ]
Wu W. [1 ]
Bai H. [1 ]
Wang Q. [1 ]
Xiong X. [1 ]
机构：
[1] School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 07期
关键词：
deep neural networks; solid oxide fuel cell; stack temperature field measurement; support vector machine; temperature sensor;
D O I：
10.19912/j.0254-0096.tynxb.2022-0306
中图分类号：
学科分类号：
摘要：
Based on the quartz fiber temperature sensor and the Cartesian coordinate manipulator，this paper designs and builds the SOFC stack temperature field measurement system. Then，the temperature data of the cathode port in the emulation SOFC stack are measured through the built measurement system. Based on the collected data，the emulation stack temperature field model is established based on deep neural network method，and compared with the stack temperature field model based on support vector machines（SVM）. The results show that the stack temperature field model with deep neural network owns shorter training time and higher prediction accuracy. Its mean absolute prediction error and root mean square error are 45.2% and 47.4% respectively of the stack temperature field model of support vector machines，which is more convenient for the application of SOFC stack temperature field modelling in this paper. © 2023 Science Press. All rights reserved.
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页码：55 / 60
页数：5
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