气固循环流化床颗粒浓度波动信号的预测  

Prediction of Solids Holdup Time Series of a GasSolid Circulating Fluidized Bed

作　　者：李晓祥[1] 石炎福[1] 黄卫星[1] 余华瑞[1] 祝京旭[2] 

机构地区：[1]四川大学化工学院,四川成都610065 [2]西安大略大学化工系

出　　处：《过程工程学报》2003年第1期8-13,共6页The Chinese Journal of Process Engineering

基　　金：国家自然科学基金--海外青年基金资助项目(编号: 29928005)

摘　　要：结合重构相空间方法与人工神经网络法,建立了混沌时间序列预测模型. 应用此模型对f100 mm16 m的上行气固循环流化床系统中的FCC固体颗粒局部颗粒浓度波动信号进行了预测. 结果表明:循环流化床的颗粒浓度波动信号只能被短期预测,其长期行为是不可预测的,这从另一个角度说明气固循环流化床系统是一混沌系统.A prediction model based on combination of phase space reconstruction with artificial neural network was proposed. In this model, the Takens phase space reconstruction method is used to reconstruct the attractors, which represent the system hydrodynamics from the single variable time series, and the radial based function artificial neural network is used to fit the attractors in phase spaces. Firstly, the model was verified by the Lorenz chaotic system. Then the model was used to predict the local solids holdup fluctuation in a circulating fluidized bed riser with 16 m height and 0.10 m ID. The experiments were conducted with FCC particles and superficial gas velocity ranging from 3.5~8.2 m/s and the solids circulating fluxes ranging from 50~202 kg/(m2.s). The local solids holdup time series signals were measured at 900 Hz using an optical fiber probe. The results showed that (1) the proposed model based on phase space reconstruction and the artificial neural network is a useful method in predicting local solids holdup fluctuation in gas-solid circulating fluidized beds; (2) the predictability of solids holdup in the CFB is possible only in a short-time, in accordance with that the circulating fluidized bed is a chaotic system.

关 键 词：循环流化床 颗粒浓度波动 预测 重建相空间 人工神经网络 

分 类 号：TQ021.1[化学工程]