中国电机工程学报2025，Vol.45Issue(19)：7634-7643,中插39,11.DOI:10.13334/j.0258-8013.pcsee.241152
燃煤锅炉屏式过热器超温预测的数据驱动建模
Data-driven Modeling for Over-temperature Prediction of Platen Superheater in Coal-fired Boiler
樊昱晨 1周永清 1韦昌 1刘欣 2王赫阳1
作者信息
- 1. 天津大学机械工程学院,天津市津南区 300354
- 2. 烟台龙源电力技术股份有限公司,山东省 烟台市 264006
- 折叠
摘要
Abstract
Air-staging combustion technology is widely used in coal-fired boilers to reduce NOx emissions.However,it causes high-temperature flame to move upwards in the furnace and aggravates the tube over-temperature of the platen superheater(PLSH)located in the upper furnace.Moreover,the rapid changes of unit load under load-cycling mode further aggravate the tube over-temperature of boiler PLSH.In order to guide the safe and reliable operation of boilers,a deep neural network model with hyperparameters optimized by genetic algorithm(GA-DNN)is proposed to predict the over-temperature of boiler PLSH.By establishing the mapping correlation between the air,coal,and steam parameters and 30 tube panel temperatures at PLSH-outlet,the model can accurately predict the temperature distribution of the PLSH under different load conditions.On this basis,it can identify the over-temperature operational conditions(＞550 ℃)of PLSH for the present and for the next 5 min with over 97.5％precision.Additionally,it can predict the region with the worst PLSH in the next 5 min,with an accuracy rate of 89.2％.关键词
燃煤锅炉/屏式过热器/超温预测/深度神经网络/遗传算法/超参数优化Key words
coal-fired boiler/platen superheater/over-temperature prediction/deep neural network/genetic algorithm/hyperparameters optimization分类
能源科技引用本文复制引用
樊昱晨,周永清,韦昌,刘欣,王赫阳..燃煤锅炉屏式过热器超温预测的数据驱动建模[J].中国电机工程学报,2025,45(19):7634-7643,中插39,11.
