Modelling the SOFC behaviours by artificial neural network
| Autor: | Konrad Świrski, Jarosław Milewski |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Network architecture
Artificial neural network Cell voltage Renewable Energy Sustainability and the Environment Computer science Computer Science::Neural and Evolutionary Computation Process (computing) Energy Engineering and Power Technology Cell behaviour Condensed Matter Physics Backpropagation Volumetric flow rate Fuel Technology Control theory Solid oxide fuel cell |
| Zdroj: | International Journal of Hydrogen Energy. 34:5546-5553 |
| ISSN: | 0360-3199 |
| DOI: | 10.1016/j.ijhydene.2009.04.068 |
| Popis: | The Artificial Neural Network (ANN) can be applied to simulate an object's behaviour without an algorithmic solution merely by utilizing available experimental data. The ANN is used for modelling singular cell behaviour. The optimal network architecture is shown and commented. The error backpropagation algorithm was used for an ANN training procedure. The ANN based SOFC model has the following input parameters: current density, temperature, fuel volume flow density (ml min−1 cm−2), and oxidant volume flow density. Based on these input parameters, cell voltage is predicted by the model. Obtained results show that the ANN can be successfully used for modelling the singular solid oxide fuel cell. The self-learning process of the ANN provides an opportunity to adapt the model to new situations (e.g. certain types of impurities at inlet streams etc.). Based on the results from this study it can be concluded that, by using the ANN, an SOFC can be modelled with relatively high accuracy. In contrast to traditional models, the ANN is able to predict cell voltage without knowledge of numerous physical, chemical, and electrochemical factors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect