摘要
在对锅炉飞灰含碳量进行人工神经网络建模的基础上,确定了各种运行参数和煤种对锅炉飞灰含碳量的影响关系。由于锅炉煤种的多变性,针对某个煤种进行实炉调整所获得的最佳工况往往与目前燃用煤种所需的最佳工况偏离。文中结合遗传算法和人工神经网络技术,对某台300MW四角切圆燃煤电厂锅炉热效率的优化进行了研究,为大型电厂锅炉通过燃烧调整提高锅炉效率提供有效手段。
By using a neural network to model the unburned carbon content in the fly ash from a high-capacity boiler, a relationship between the unburned carbon content and the boiler operation parameters is obtained. Because of the time-to-time variation of the coal quality, however, the boiler sometimes deviates its optimal condition of operation designed for specific coal. Combining to use the neural network with a genetic algorithm, we can find the best operation parameters for getting a highest boiler efficiency. A 300MW power plant boiler is taken as an example to show the procedure of the computation and optimization, which leads to a powerful approach to promote the boiler efficiency by adjusting the boiler and its combustion in a large power plant.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2002年第7期125-128,共4页
Proceedings of the CSEE
基金
国家重点基础研究专项经费项目(G1999022204)。~~
关键词
遗传算法
燃煤锅炉
热效率
优化
utility boiler
boiler heat efficiency
genetic algorithms
