JIN Zhiyuan, LI Shengnan, TAN Peng, et al. Multi-parameter collaborative prediction model of boilers based on long-short-term memory neural network[J]. Thermal power generation, 2021, 50(5): 120-126.
JIN Zhiyuan, LI Shengnan, TAN Peng, et al. Multi-parameter collaborative prediction model of boilers based on long-short-term memory neural network[J]. Thermal power generation, 2021, 50(5): 120-126. DOI: 10.19666/j.rlfd.202007234.
Cooperative control of boiler is an effective approach to improve steam temperature stability under flexible operation. Taking a 660 MW coal-fired boiler as the research object
by using the historical operation data
a cooperative prediction model based on the long-short-term memory(LSTM) neutral network for main steam temperature
reheated steam temperature
NO_x and CO mass concentration at the furnace outlet was established. The prediction results show that
all the correlation coefficients of the four outputs of the cooperative prediction model are larger than 0.94
indicating the model has good comprehensive prediction effect and generalization ability. The research provides a foundation for collaborative optimization control of boiler steam temperature
pollutants emission reduction and furnace efficiency enhancement.