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The overheating of boiler heating surface seriously affects the safe operation of the power plant. It is of great significance for the safety of boiler to predict the tube temperature of heating surface and to take appropriate preventative measures. A data driven-based model for tube temperature prediction is proposed in this study. Firstly, the key variables affecting the tube temperature are selected by grey correlation analysis that affect the wall temperature of the heating surface, and a wall temperature prediction model based on long short term memory (LSTM) neural network is constructed. Then, the correlation feature coefficients under similar historical operating conditions are defined, and the predicted wall temperature obtained by the LSTM neural network is corrected to improve the model's prediction accuracy. Finally, an on-duty supercritical boiler with 600 MW capacity is taken as the case study. Results showed that the relative error of the proposed prediction model is within (−2.5%, 2.5%). The average relative error is 0.40%, and the average tube temperature prediction error is 2.24 ℃. It indicates that the proposed model is helpful for the tube temperature prediction of the boiler under complex operating conditions.
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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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