摘要
随着锅炉行业的发展,燃煤锅炉燃烧技术不断完善优化,在这样的背景之下,效率的提升也逐渐成为衡量锅炉运行状况的关键指标。因此,对基于人工神经网络(Artificial Neural Network,ANN)的火电厂燃煤锅炉燃烧优化方法设计与研究。进行人工神经网络下燃烧数据预处理,核算锅炉效率的同时,构建人工神经网络交互优化模型,采用遗传算法改进完成燃煤锅炉燃烧的优化处理。最终的测试结果表明:在不同的时刻环境下,与两种传统方法对比,所设计的人工神经网络锅炉燃烧测试组最终得出的实际蒸发比相对较高,表明其燃烧的速度与质量更佳,具有实际的应用价值。
With the development of boiler industry,the combustion technology of coal-fired boiler is constantly improved and optimized.Under this background,the improvement of efficiency has gradually become the key index to measure the operation status of boiler.Therefore,the combustion optimization method of coal-fired boiler in thermal power plant based on Artificial Neural Network(ANN)is designed and studied.While preprocessing the combustion data under the ANN and calculating the boiler efficiency,the interactive optimization model of ANN is constructed,and the optimization processing of coal-fired boiler combustion is completed by using genetic algorithm.The final test results show that in different time environments,compared with the two traditional methods,the actual evaporation ratio of the ANN boiler combustion test group designed in this paper is relatively high,indicating that its combustion speed and quality are better and has practical application value.
作者
黄祖光
胡海舰
徐杰
徐会咏
吉喆
李晓明
HUANG Zuguang;HU HaiJian;XU Jie;XU Huiyong;JI Zhe;LI Xiaoming(Fenyi power plant,Shuanglin Town,Fenyi County,Xinyu 338000,China)
出处
《通信电源技术》
2021年第24期159-162,共4页
Telecom Power Technology
关键词
人工神经网络
火电厂
燃煤锅炉
燃煤燃烧
优化方法
自然反应
Artificial Neural Network(ANN)
thermal power plant
coal fired boiler
coal burning
optimization method
natural reaction
