基于不同ANN模型对流化床颗粒内循环量的预测研究

Prediction of Internal Circulation of Particles in Fluidized Bed Based on Different ANN Models

双循环流化床的颗粒内循环量直接决定了副流化床内的传热效果。为了解双流化床在其他工况下的传热效果,采用多种ANN模型对颗粒的内循环量进行预测。以一种新型双流化床冷态实验系统的60组工况运行结果作为数据来源,采用5种不同的人工神经网络(ANN)模型对颗粒内循环量进行预测,并同各个工况下的实验数据进行对比。采用主流化床流化风速、炉膛隔墙高度、主流化床初始床层高度这三个因素作为模型的特征值,将颗粒内循环量作为输出目标,以平均绝对百分比误差和均方根误差作为评价指标。结果显示GA-BP、SVM以及ELM三种模型具有比较理想的预测精度。

The internal circulation of particles in a dual circulating fluidized bed directly determines the heat transfer effect in the sub fluidized bed.In order to understand the heat transfer effect of double fluidized bed under other conditions,a variety of ANN models were used to predict the internal circulation of particles.Based on the data from results of 60 sets of operating conditions with a new dual fluidized bed cold state experimental system,five different artificial neural network(ANN)models were used to predict the internal circulation of particles,and the experimental data were compared with each other.The flow velocity,the height of furnace partition wall and the initial bed height of the main stream bed were used as the characteristic values of the model.The internal circulation of particles was taken as the output target,and the average absolute percentage error and root mean square error were used as evaluation indexes.The result shows that GA-BP,SVM and elm have ideal prediction accuracy.