【摘要】 从分散控制系统 (DCS)上采集数据 ,结合人工神经网络的非线性动力学特性和自学习特性 ,通过对锅炉输出参数和NOx 输出特性的样本学习 ,建立了大型电站燃煤锅炉的氮氧化物排放特性神经网络模型 ,并利用遗传算法实现锅炉的低NOx 燃烧的优化运行指导。系统采用Browser/Server方式 ,实现基于internet/intranet模型下的大型电站锅炉燃烧优化指导更多还原

【Abstract】 For large capacity utility boilers, a neural network model of NO x emission characteristics was established based on data acquisition from the distributed control system, combining with the non-linear dynamic property and self-learning feature of the artificial neural network, passing through sample learning of output parameters of the boiler and its NO x emission characteristics, and the system was realized by using genetic algorithm for guiding the optimization of operation under low NO x combustion in the boiler. The said system is adopting Browser/server mode, to implement combustion optimization based on Internet/Intranet programming for the said large-capacity utility boiler.更多还原