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Main steam temperature is a key parameter for the boiler of coal-fired power plants. It is difficult to remain stable under extreme load changes such as deep peak-shaving. To solve this problem, a predictive control model is added to the existing temperature loop. The proposed control system targets an ultra-supercritical boiler. A hybrid long short-term memory (LSTM) network forms the core predictor of the predictive model. A hyper parameter transfer method speeds up global optimization, avoids local optima and cuts optimization calculation amount by 88%. The predictive model gives a root-mean-square error of 0.495 ℃ and a mean absolute percentage error of 0.082%. MATLAB simulations show that the predictive control system reduces peak overshoot by 50% under extreme conditions, while preserving the control-loop stability during normal operation through multi-condition piecewise control. These results demonstrate that the proposed model predictive control system satisfies the main steam temperature regulation requirements across all 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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