基于粒子群算法的锅炉燃烧效率的优化研究  

Based on Particle Swarm Optimization of Boiler Thermal Efficiency in Power Station

作　　者：白晓旭[1,2] 王璋奇[1] 董泽[1] 

机构地区：[1]华北电力大学河北省发电过程仿真与优化控制重点实验室,河北保定071003 [2]北京交通大学机械与电子控制工程学院,北京100044

出　　处：《计算机仿真》2014年第2期441-444,共4页Computer Simulation

摘　　要：以印尼某电厂的锅炉运行DCS数据为样本,采用神经网络的方法建立了锅炉燃料、风量和锅炉燃烧效率的数学模型,并采用粒子群优化算法对燃料和风量进行优化,使锅炉的燃烧效率提高。仿真结果表明,神经网络建模和粒子群优化可以用于锅炉燃烧方面的研究,可提高锅炉燃烧的效率,并为对锅炉燃烧的深入研究奠定了基础。The boiler thermal efficiency model about the boiler fuel and air volume has been obtained with the training of neural network based on the DCS data of a power plant in Indonesia. And then the particle swarm optimization algorithm is used to optimize the fuel and air volume in order to increase the combustion efficiency of boiler. Simulation results show that the neural network model and particle swarm optimization not only can be used for boiler combustion research, but also can improve the efficiency of the boiler combustion; The study has laid the foundation for the in - depth researching of boiler combustion.

关 键 词：锅炉燃烧效率 神经网络 粒子群优化算法 

分 类 号：N945.12[自然科学总论—系统科学] TP391.9[自动化与计算机技术—计算机应用技术]