燃煤电站锅炉及工业窑炉三维燃烧温度分布监测研究进展

Research progress on monitoring three-dimensional temperature distributions in coal-fired boilers and industrial furnaces

在碳中和背景下,燃煤发电机组深度调峰及灵活性运行对炉内三维燃烧状况实时监控提出了迫切要求。总结了燃煤电站锅炉及工业窑炉三维燃烧温度分布监测研究进展。在燃烧火焰辐射成像模型方面,重点介绍了以蒙特卡洛方法为基础建立的方向辐射强度计算的DRESOR法以及近期对DRESOR法的优化,为提高燃烧介质温度的反演精度、同时反演燃烧介质的辐射特性参数分布奠定了基础。三维温度场和辐射参数同时反演问题求解的基本方法为采用Tikhonov正则化方法从多种单色辐射强度图像中重建炉内温度分布,再用最优化方法更新颗粒介质辐射特性,迭代求解。近期,反演重建算法有了新进展,新算法分3个阶段:(1)假设炉内吸收系数、散射系数和炉壁反射率分布均匀,优化求解得到最佳辐射参数及炉内温度分布;(2)在第1阶段基础上,将炉内吸收系数、散射系数设置为空间坐标的二阶多项式拟合分布,壁面仍为均匀反射率,进一步优化迭代计算;(3)在第2阶段计算收敛的基础上,进一步假设炉壁反射率为壁面坐标的二阶多项式分布,再优化迭代计算。依据反演算法最新进展获得了燃烧温度重建误差1%以内的重建结果,并实现了基于辐射参数的炉内煤粉浓度相对分布的重建。炉内三维温度场可视化监测系统在200、300、600 MW燃煤电站锅炉燃烧监控中得到了工业应用,并进一步扩展应用到轧钢厂步进式加热炉、石油化工厂管式加热炉、单火嘴燃烧炉、化工厂裂解炉等燃油或燃气工业窑炉中,应用前景良好。未来需采用机器学习和人工智能理论进一步提升耦合重建问题的求解效率,与炉内工况及热力系统三维实时、动态建模相结合,实现炉内三维工况分布参数(炉内气氛、颗粒物、污染物、炉内热负荷、炉壁热负荷分布等)实时监测及诊断和锅炉水冷壁内水动力、热力系统分布参数建模预测,构建多时间尺度大数据驱动的燃煤发电机组数字孪生系统,为开发智能锅炉/工业窑炉优化控制系统做出贡献。

Under the background of carbon neutrality, the deep peak shaving and flexible operation of coal-fired generating units put forward urgent requirements for real-time monitoring of the three-dimensional combustion conditions in the furnace. This paper summarized the research progress of three-dimensional combustion temperature distribution monitoring of coal-fired power plant boilers and industrial furnaces. As for the radiation imaging model of combustion flame, the DRESOR method for directional radiation intensity calculation based on Monte Carlo method and the recent optimization of DRESOR method were mainly introduced, which laid a good foundation for improving the inversion accuracy of combustion temperature and inversion of the distribution of radiation characteristic parameters of combustion medium. The basic method to solve the simultaneous inversion problem of three-dimensional temperature field and radiation parameters is to reconstruct the temperature distribution in the furnace from the monochromatic radiative intensity images by Tikhonov regularization method. Then the radiative properties of the particle medium are updated with optimization method and solved iteratively. Recently, there have been new developments in the inversion algorithm. The new algorithm can be divided into three stages. Firstly, assuming uniform distribution of absorption coefficient, scattering coefficient and reflectivity of the furnace wall, the optimal radiation parameters and temperature distribution inside the furnace are obtained by the optimization solution. Secondly, on the basis of stage 1, the absorption and scattering coefficients in the furnace are set as second-order polynomial fitting distributions in spatial coordinates, and the walls are still set with uniform reflectivity to further optimize the iterative calculation. Finally, on the basis of the convergence of the calculation in stage 2, the second-order polynomial distribution of the reflectivity of the furnace wall in wall coordinates is further assumed, and then the calculation is optimized iteratively. The latest development of the inversion algorithm has obtained the reconstruction result of the combustion temperature with reconstruction error within 1%, and realizes the reconstruction of the relative distribution of pulverized coal concentration in the furnace based on the radiative properties. The monitoring systems of three-dimensional temperature field in the furnace has been industrially applied in the combustion monitoring of 200, 300 and 600 MW coal-fired power plant boilers and further expanded to oil-fired or gas-fired industrial kilns such as walking furnace in rolling mill, tubular furnace in petrochemical plant, single burner combustion furnace and cracking furnace in chemical plant, showing a good application prospect. In the future, machine learning and artificial intelligence theory need to be adopted to further improve the efficiency of solution of the coupled reconstruction problem, combined with three-dimensional real-time and dynamic modeling of furnace conditions and thermal system, to realize real-time monitoring and diagnosis of the parameters of the three-dimensional furnace conditions distribution(furnace atmosphere, particulate matter, pollutants, furnace heat load, furnace wall heat load distribution, etc.) and modeling and prediction of the parameters of the distribution of hydrodynamic and thermal systems in the boiler water wall, to further build a multi-timescale big data-driven digital twin system for coal-fired generating units, contributing to the development of the smart boiler and furnace optimization control system.