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。
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。
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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