循环流化床锅炉是一种具有广泛应用前景的高效清洁燃烧技术,但磨损问题一直制约循环流化床锅炉的长周期运行。目前大多通过运行勘测经验积累预判磨损状况或通过数值模拟得到速度场与浓度场了解磨损分布区域,鲜有学者通过理论方法定量研究预测运行锅炉炉内不同区域的磨损状况。磨损主要受粉尘气流速度与浓度影响，基于运行数据利用流体力学软件模拟得到50组工况下的水冷壁受热面附近的飞灰颗粒速度和飞灰颗粒浓度数据，通过BP神经网络和遗传算法对某石化厂CFB锅炉掺烧石油焦建立结构为5-13-12的GA-BP神经网络的相对磨损量磨损预测模型,进而分析布风板风量、一次风风量、二次风风量、燃料量以及掺混比5个运行参数对炉膛内不同区域磨损的影响。结果表明：测试集预测结果与现场勘测的磨损状况较吻合，验证了采用GA-BP神经网络建立磨损预测模型的可行性，指导防磨运行，即在保证锅炉正常运行情况下，适当减少一定量的布风板流化风，减少密相区一、二次风以及燃料量，适当增加石油焦掺混比，可减轻炉内受热面的磨损。

Circulating fluidized bed (CFB) boiler is a highly efficient and clean combustion technology with a wide range of applications, but the wear problem has always plagued the long-term operation of the CFB boiler. At present, the wear conditions in different areas are mostly master through operational survey experience or by using numerical simulations to obtain velocity and concentration fields，few scholars have studied quantitatively the wear conditions of different areas in the boiler through theoretical methods. The wear is mainly affected by the velocity and concentration of dust airflow. An attempt was made to obtain the velocity and concentration of fly ash particles in the vicinity of the water-cooled wall heating surface under 50 sets of operating conditions using hydrodynamic software simulations in this study, the relative wear prediction model of GA-BP neural network with the structure of 5-13-12 was established by BP neural network and genetic algorithm (GA) for the CFB boiler mixed with petroleum coke in a petrochemical plant. In turn, the effects of five operating parameters, namely the air volume of the air distribution plate, the primary air volume, the secondary air volume, the fuel volume, and the blending ratio, on the wear in different areas of the furnace chamber were analyzed. The results show that the prediction results of the test set are in good agreement with the wear conditions surveyed on-site, which verifies the feasibility of using GA-BP neural network to establish a wear prediction model and can guide the anti-wear operation, under the condition of ensuring the normal operation of the boiler, appropriately reducing a certain amount of fluidized air of the air distribution plate, reducing the amount of primary and secondary air and fuel in the dense phase area, and appropriately increasing the petroleum coke blending ratio, which can reduce the wear of the heated surface in the furnace.

0 引言

1 磨损预测模型建立

   1.1 GA-BP神经网络

   1.2 磨损模型

2 数值模拟及工况数据

   2.1 网格划分

   2.2 数学模型

   2.3 边界条件

   2.4 数据采集

3 GA-BP预测

   3.1 GA-BP神经网络预测模型

   3.2 相对磨损量

4 运行参数对炉膛内磨损的影响

   4.1 布风板风量对炉内磨损的影响

   4.2 密相区一次风风量对炉内磨损的影响

   4.3 二次风风量对炉内磨损的影响

   4.4 燃料量对炉内磨损的影响

   4.5 石油焦掺混比对炉内磨损的影响

5 结论