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。
Solids holdup distribution and liquid backmixing coefficients are fundamental to the scale-up design and optimization of liquid-solid magnetically stabilized reactors.
固含率分布和液相返混系数是液固磁稳定反应器放大与优化所必需的基础数据。
Solids holdup distribution and liquid backmixing coefficients are fundamental to the scale-up design and optimization of liquid-solid magnetically stabilized reactors.
固含率分布和液相返混系数是液固磁稳定反应器放大与优化所必需的基础数据。
应用推荐