Technique improvement of circulation fluidized bed system and water supply system 循环流化床系统及给水系统的技术改造
The chaos studies indicate that fluidized bed systems are determinacy chaos systems and the results of the dissipative structure theory attribute fluidized bed systems to non - equilibrium thermodynamic ones 混沌理论研究表明流化床系统是确定型的混沌系统,而耗散结构理论的结果认为流化床系统属于非平衡热力学系统。
Parameter measurement of fluidization was important and needed to be solved imminently . many researchers devoted to it but had gained a little achievement because of the complexity of fluidization 虽然有不少学者多年来一直致力于这方面的研究,但由于流化床系统本身的复杂性,使得大多数参数检测方法未能在工业现场得到应用。
Finally , bp neural network recognition model of particulate and aggregative fluidization and rbf neural network prediction model for chaotic time series of circulating fluidized bed have been set up , which provides new methods for on - line recognition of fluidization state and control and prediction of circulating fluidized bed systems 最后建立了散式流化和聚式流化bp神经网络识别模型和循环流化床中的混沌时间序列的rbf神经网络预测模型,为流型在线识别和循环流化床系统的控制和预测等提供了新的方法。
In this paper , the mutual information theory and non - linear hydrodynamic theory are mainly used in our studies . based on a large amount of experimental data , different signals of six channels are analyzed together at wide operation gas velocities . in the view of mutual information , information transport series between six channels are computed 研究从互信息的角度出发,分析计算了六个通道两两之间的信息传输时间序列,得到了传统的单参数检测所不能提取的信息,从新的角度认证了气固流化床系统的非线性本质。