基于GA-BP的煤质发热量在线监测

Abstract

电力用煤由于受到产地、运输和价格等多种因素影响，种类变化繁多，煤质发热量有很大的变化。这对锅炉的安全经济运行有很大的影响。为了及时了解机组的运行状况，本文应用反向建模思想，结合遗传算法优化的BP神经网络，模拟出锅炉运行过程中易测量的参数值和煤质发热量的关系，构建了煤质发热量在线测量模型。用电厂运行数据进行模型验证，精度满足工程要求，切实可行。 Due to a variety of factors, such as the origin, transport and price, the types of steam coal for power changed in a large range. Thus coal calorific value varied widely. It has a great impact on the safe operation of the boiler. In order to keep abreast of the unit’s operating conditions, this thesis applied reverse modeling ideas, combined with Genetic Algorithm optimizing BP neural network to simulate the relationship between the parameter values easily measured in the process of boiler operation and coal heat, and constructed an online coal calorific value monitoring model. The result of model validation with plant operation data showed that the accuracy met the engineering requirements, and the model was practical.