基于智能计算的锅炉燃烧优化指导系统及其应用

提出一基于智能计算技术的锅炉燃烧优化实时指导系统,该系统根据电科院现场燃烧调整试验数据以及锅炉运行历史数据,采用人工智能神经网络建立了锅炉燃烧特性的NSGA-Ⅱ数学模型,将现场燃烧调整试验数据结果和日常司炉的运行经验模型化,并采用多目标遗传算法优化技术从模型中提取专家级燃烧运行知识和经验,通过计算机在线指导锅炉配风、配煤等燃烧运行调整,达到同时提高锅炉运行效率和减小NO_x排放的目的。

Guiding System for Boiler Combustion Optimization Based on Artificial Neural Network and Its Application

In this paper, a real-time guiding system for boiler combustion optimization based on artificial intelligence technology is proposed, in which the NSGA-II mathematical model is established by adopting neural artificial intelligence network in combination with the data of coal-fired boiler combustion adjustment test and historical operation. By using this approach, the modeling of the field combustion adjustment test data and the operator’s daily experience is realized, and the expert knowledge and experience are obtained with the multi-objective genetic algorithm. Consequently, through the online computational guidance for the adjustment of combustion air and fuel, the goal of improving the boiler efficiency and reducing the NO_x emissions are also reached simultaneously.