A comparison of the artificial neural network model and the theoretical model used for expressing the kinetics of electrophoretic deposition of YSZ on LSM

研究成果: Article

6 引文 (Scopus)

摘要

In the present study, an artificial neural network (ANN) model and a theoretical model are established to predict the kinetic behavior of electrophoretic deposition (EPD). Both the theoretical model and the ANN model describe the kinetic behavior of EPD at a low-applied voltage (below 15 V) well. However, the theoretical model failed to predict the behavior at the higher applied voltages of 40 and 50 V. In contrast, the proposed ANN model not only showed enhanced numerical accuracy, but was also generic to other operational conditions as well. Compared to the theoretical model, the ANN model shows outstanding capability of predicting actual kinetic behavior.

原文English
頁(從 - 到)338-344
頁數7
期刊Journal of Power Sources
175
發行號1
DOIs
出版狀態Published - 2008 一月 3

指紋

yttria-stabilized zirconia
Neural networks
Kinetics
kinetics
Electric potential
low voltage
high voltages

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Physical and Theoretical Chemistry
  • Electrical and Electronic Engineering

引用此文

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abstract = "In the present study, an artificial neural network (ANN) model and a theoretical model are established to predict the kinetic behavior of electrophoretic deposition (EPD). Both the theoretical model and the ANN model describe the kinetic behavior of EPD at a low-applied voltage (below 15 V) well. However, the theoretical model failed to predict the behavior at the higher applied voltages of 40 and 50 V. In contrast, the proposed ANN model not only showed enhanced numerical accuracy, but was also generic to other operational conditions as well. Compared to the theoretical model, the ANN model shows outstanding capability of predicting actual kinetic behavior.",
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AU - Ciou, Sian Jie

AU - Fung, Kuan-Zong

AU - Chiang, Kai-Wei

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AB - In the present study, an artificial neural network (ANN) model and a theoretical model are established to predict the kinetic behavior of electrophoretic deposition (EPD). Both the theoretical model and the ANN model describe the kinetic behavior of EPD at a low-applied voltage (below 15 V) well. However, the theoretical model failed to predict the behavior at the higher applied voltages of 40 and 50 V. In contrast, the proposed ANN model not only showed enhanced numerical accuracy, but was also generic to other operational conditions as well. Compared to the theoretical model, the ANN model shows outstanding capability of predicting actual kinetic behavior.

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