哈尔滨理工大学 自动化学院,哈尔滨,150080
纸质出版:2016
移动端阅览
宋清昆, 李源松. RBF神经网络锅炉燃烧系统建模[J]. 哈尔滨理工大学学报, 2016,21(1):89-92.
SONG Qing-kun, LI Yuan-song. Modeling of the Boiler Combustion System by RBF Neural Networks[J]. 2016, 21(1): 89-92.
宋清昆, 李源松. RBF神经网络锅炉燃烧系统建模[J]. 哈尔滨理工大学学报, 2016,21(1):89-92. DOI: 10.15938/j.jhust.2016.01.018.
SONG Qing-kun, LI Yuan-song. Modeling of the Boiler Combustion System by RBF Neural Networks[J]. 2016, 21(1): 89-92. DOI: 10.15938/j.jhust.2016.01.018.
针对锅炉燃烧控制系统的研究和试验,确定锅炉燃烧调节优化控制方案,使发电机组安全经济稳定运行,提高锅炉燃烧效率的问题,是电厂锅炉优化的主要目标.基于大庆某热电厂锅炉的运行数据,针对其优化的控制回路,采用径向基神经网络的方法建立了以蒸汽压力、烟气含氧量和炉膛负压为输出的预测模型,实现了上述3个参数的预测,结果表明建立的模型能够反映锅炉的特性。为下面的优化工作奠定了基础.
The research and test of boiler combustion control system determine the optimal control of boiler combustion adjustment scheme
make economic stability operation of the generator set
improve the efficiency of boiler combustion
and are one of the main targets of the power plant boiler optimization. Based on Daqing thermal power plant boiler operation data
aiming at the optimization of control circuit
by RBF neural network established steam pressure
flue gas oxygen content and furnace negative pressure for the output prediction model
we impvove the above three parameters prediction. The results show that the model established can reflect the characteristics of the boiler
and the foundation for the following optimal work.
0
浏览量
736
下载量
CSCD
关联资源
相关文章
相关作者
相关机构