A new hybrid method for prediction of solids holdup in gas-solid circulating fluidized bed is proposed based on chaos phase reconstruction and wavelet package as well as neural networks.

提出了一种将混沌的相空间重构、小波包分析和神经网络相结合的新方法用于预测气-固循环流化床的颗粒浓度。

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The solids density and solids residence time in circulating fluidized bed increase with increasing solids circulation rate and reducing gas rate.

降低气速、提高颗粒循环速率，可以提高床内颗粒浓度，增加颗粒停留时间。

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