基于遗传算法和神经网络的电站锅炉燃烧优化

燃烧优化技术是实现电站锅炉高效燃烧和污染物控制的最经济、最有效的方法之一。本文首先利用神经网络建立起电站锅炉燃烧特性模型，然后利用遗传算法计算送风调节控制系统最优氧量设定值。仿真结果表明采用本文设计的燃烧优化策略，不仅可以提高燃烧效率而且能有效降低排放烟气中的氮氧化物含量，具有较高的实用价值和广泛的应用前景。

Power Plant Boiler Combustion Optimization Based on Genetic Algorithms and Neural Networks

Combustion optimization technology is one of most economical and effective way to achieve power plant boiler highly efficient combustion and pollution control. This paper uses neural network to establish the power plant boiler combustion model, and uses genetic algorithm to Calculate air conditioning control system for optimal oxygen settings. In this paper, simulation results show that the use of strategies designed to optimize combustion, not only can improve the combustion efficiency and can effectively reduce emissions of nitrogen oxides in flue gas content, have a high practical value and broad application prospects.