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Outline
Solid oxide fuel cell (SOFC) has attracted increasing attention due to its environmental friendliness, high efficiency and broad prospects. The life prediction of SOFC system is critical to the long-term operation of power generation system. However, the accuracy in predicting the life of SOFC using long short-term memory (LSTM) neural network model with numerous parameters is still insufficient. To address this problem, the orthogonal experiment method was employed to systematically optimize the parameters for LSTM. The results indicate that batch size has the greatest influence on the prediction accuracy and calculation time of LSTM. And the root mean square error (RMSE) of optimized model is reduced by 72.36%、18.18%、35.05% and 32.97% when compared with that of recurrent neural network (RNN), gated recurrent unit (GRU), whale optimization algorithm (WOA) optimizates LSTM (WOA-LSTM) and grey wolf optimization (GWO) optimizates LSTM (GWO-LSTM), respectively. The systematically optimized LSTM model exhibits improved performance, which may mitigate fuel cell degradation with other control strategies.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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