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In order to monitor the condition of the heat recovery steam generator (HRSG) and to ensure the healthy operation of the HRSG, the three-pressure main steam temperature and pressure prediction model was established by using the data from the healthy operation of HRSG and combining the three methods of principle component analysis (PCA), sparrow search algorithm (SSA) and long short-term memory (LSTM). PCA was used to reduce the input parameters of the model from 22 to 9 dimensions, and taking the reheat steam temperature prediction model as an example, it was concluded that the model with PCA dimensionality reduction reduced the hyperparameter optimization time by 11.3% compared with the model without PCA dimensionality reduction. Compared with the model without SSA, the value of coefficients of determination of these models is significantly improved, mean absolute error and root mean square error are significantly reduced, and the alarm threshold of the main steam temperature HRSG is determined according to the distribution of absolute error. Therefore, the condition monitoring model of HRSG based on PCA-SSA-LSTM has short training time and high prediction accuracy, and the model provides theoretical basis and technical support for fault monitoring and diagnosis of HRSG in gas turbine combined cycle power plants.
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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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