摘要
煤粉锅炉炉膛火焰温度场的测量是燃烧调整的基础,对于锅炉的燃烧经济性、安全性诊断以及优化运行有着重要的意义.利用4个面阵CCD获取的火焰图像的数字信号,在分析测试系统物理模型和炉膛火焰温度分布规律的基础上,建立了非线性多目标优化数学模型,并应用PSO算法和文中提出的微粒群-鲍威尔混和算法(PSO-Powell)对其进行了求解.并在某电站350 MW锅炉上进行了多负荷工况下的实际测试.结果表明,此混合算法提高了PSO算法的寻优能力,利用其重建的温度场可以作为燃烧诊断和优化运行的重要依据.
The measurement of flame temperature field is very important for the regulation, diagnosis and optimization of a coal-fired boiler. With 4 CCD cameras, digital data of a flame image can be obtained, which represent radiative heat transfer along ray beam. In this paper, based on the theories of geometric optics and radiative transfer, a reconstruction technique for the section temperature field of furnace, combined with hybrid particle swarm optimization-Powell （PSO- Powell） algorithm, was developed. To validate the model, numerical simulation was carried out based on PSO algorithm and hybrid PSO-Powell algorithm respectively. Furthermore, a series of experiments were performed in a 350 MW power plant. The results indicate that the algorithm proposed in this paper can improve the ability to find optimization solution of PSO algorithm, and the reliability and prospective application of the model in the diagnosis and optimization of a coal-fired boiler.
出处
《燃烧科学与技术》
EI
CAS
CSCD
北大核心
2008年第6期551-556,共6页
Journal of Combustion Science and Technology
