中文摘要：

在锥形布风板双循环流化床冷态装置上，研究了提升管风速、气化室风速、物料质量和颗粒粒径对提升管颗粒循环流率的影响，并与水平布风板的结果进行了对比．利用3种改进的BP神经网络算法建立模型来预测循环流率．结果表明：提升管颗粒循环流率随着提升管风速和气化室风速的增大而增大，当风速达到一定值后，增大趋势逐渐平缓；循环流率随着物料质量的增大基本呈线性增大，随着颗粒粒径的增大而明显减小；锥形布风板比水平布风板更具优势，同样条件下可以增大循环流率；BFGS拟牛顿算法的预测效果最佳，其颗粒循环流率预测值与实验值的最大相对误差为7．7035％，平均相对误差为3．5943％．

英文摘要：

Experimental tests were carried out on a cold-state setup of dual circulating fluidized bed （DCFB） with cone air distributor, so as to study the effects of following factors on the solids circulation rate in the riser, such as the air velocity in riser, air velocity in gasifier, material mass and particle size, etc. , of which the results were compared with that of the DCFB with horizontal air distributor. Moreover, models were built up based on three improved BP neural network algorithms to predict the solids circulation rate. Results show that the solids circulation rate increases with rising air velocity in both the riser and the gasi- tier, but the acceleration rate slows down when the air velocity reaches a certain value; the solids circula tion rate increases linearly with rising material mass, but significantly reduces with increasing particle size; cone air distributor has advantages over horizontal air distributor, which helps to raise the solids circula- tion rate under same conditions; BFGS Quasi-Newton algorithm is found to have the best prediction effect, with which the maximum relative error of solids circulation rate between predicted and experimental data is 7. 703 5% and the mean relative error is 3. 594 3%