基于TF-NGO算法的CFB锅炉床温系统建模研究

Study of CFB Boiler Bed Temperature System Modeling Based on TF-NGO Algorithm

床温是循环流化床(CFB)锅炉重要的运行参数之一。针对床温耦合性强、干扰因素多、控制复杂的问题,亟需建立床温的数学模型,以实现床温控制,从而保证CFB锅炉安全、平稳地运行。为此,首先引入了混沌映射、切线飞行(TF)和柯西变异策略改进北方苍鹰优化(NGO)算法,并用实际工况的系统模型测试TF-NGO算法。测试结果表明,TF-NGO算法拥有更快的收敛速度和更高的寻优精度。其次,采集并预处理山西某电厂350 MW超临界CFB锅炉的现场运行数据。最后,采用TF-NGO算法对所建模型的参数进行辨识,并用实际工况数据进行模型验证。辨识和验证结果表明,由TF-NGO算法辨识的床温模型与实际输出拟合度高,能有效反映床温的动态特性,证明所建模型的有效性。该研究为后续对350 MW超临界CFB锅炉床温的优化控制研究奠定了基础。

Bed temperature is one of the important operating parameters of circulating fluidized bed(CFB)boiler.In view of the problems of strong bed temperature coupling,many disturbing factors and complicated control,there is an urgent need to establish a mathematical model of bed temperature to realize the bed temperature control,to ensure the safe and smooth operation of CFB boiler.To this end,chaotic mapping,tangent flight(TF)and Cauchy variational strategies are firstly introduced to improve the northern goshawk optimization(NGO)algorithm,and the TF-NGO algorithm is tested with a system model of the actual operating conditions.The test results show that the TF-NGO algorithm has faster convergence speed and higher optimization accuracy.Secondly,the field operation data of 350 MW supercritical CFB boiler in a Shanxi power plant are collected and preprocessed.Finally,the TF-NGO algorithm is used to identify the parameters of the proposed model,and the model is validated with the actual operating condition data.The identification and validation results show that the bed temperature model identified by the TF-NGO algorithm fits well with the actual output and can effectively reflect the dynamic characteristics of the bed temperature,which proves the validity of the proposed model.This study lays foundation for the subsequent research on the optimal control of bed temperature in 350 MW supercritical CFB boilers.