提升管二次风对循环流率影响的试验及预测模型研究  

RESEARCH ON EFFECT OF SECONDARY AIR ON SOLIDS CIRCULATION RATE IN DCFB AND PREDICTION MODEL

作　　者：陈鸿伟[1] 祁海波[1] 杨新[1] 梁占伟[1] 

机构地区：[1]华北电力大学能源与动力工程学院,保定071003

出　　处：《太阳能学报》2013年第6期1109-1114,共6页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金(50876030);高等学校博士学科点专项科研基金(20090036110008)

摘　　要：设计并搭建双循环流化床冷态试验台,通过试验分析提升管二次风风量、二次风送风方式、二次风口高度和二次风口数目对颗粒循环流率的影响。建立BP神经网络预测模型,采用3种算法对颗粒循环流率进行预测,通过对比找出最优预测模型——基于LM算法的改进型BP神经网络预测模型。该预测模型很好地预测了二次风特性对颗粒循环流率的影响,试验值与模型预测值的平均绝对误差为0.23kg/(m2·s),平均相对误差仅为1.37%;最大偏差为1.23kg/(m2·s),最大相对误差5.75%。The effect of flow rate, supply mode, inlet height and inlet number of secondary air on solids circulation rate were experimentally analyzed on the self-designed DFCB cold-state test bench. A BP neural network prediction model was established, and improved BP neural network prediction model based on Levenberg- Marquardt algorithm was determined to be the optimal model to predict the solid circulation rate via comparison of three algorithms. This model made a good prediction on the relationship between characteristics of secondary air and solids circulation rate. It shows that the mean absolute error between forecasted value and experimental value is 0.23 kg/（m2 -s）, the mean relative error is merely 1.37%, the maximum diversion is 1.23kg/（m2 ＂s）, and the maximum relative error is no more than 5.75%.

关 键 词：双循环流化床 二次风 颗粒循环流率 改进型BP神经网络 预测模型 

分 类 号：TK229[动力工程及工程热物理—动力机械及工程]