宽筛分硅粉颗粒的流化特性

Fluidization characteristics of silicon particle system are studied by the pressure fluctuation method.The existence of fine particles in the system can improve fluidization. Silicon particles with a wide size distribution,preferably with some fines, behave as Group A particles according to Geldart classification, although the system belongs to Group B actually. The system is also approved to be suitable for organochlorosilane monomer production using a fluidized bed reactor. Experimental data obtained in this work are important for the design and operation of commercial fluidized bed reactors for the production of organochlorosilane monomers.     

单组分球形颗粒最小流化速度的简便计算

对Ｇｅｌｄａｒｔｓ的Ｂ．Ｄ两类物料建立了一个新的采用倾斜量筒的随意松散堆积法来简易可行地确定该单分物料随意散堆积空隙率。实验数据表明，这一随意松散堆积空隙率可用来代替最小流化空隙的率的数值，并用Ｅｒｇｕｎ方程式，求取最小流化速度。     

生物质和惰性颗粒二组分混合物的最小流化速度

实验发现纯锯末不能在流化床中流化,锯末中加入惰性固体颗粒构成锯末／玻璃珠、锯末／沙子双组分混合物可实现流化．研究了不同粒径、不同配比混合物的流化规律．已有的预测双组分混合物最小流化速度的公式不能应用于锯末／玻璃珠、锯末／沙子双组分混合物．同时研究了球形、椭圆形菜籽的最小流化速度,并研究了不同粒径菜籽与不同粒径沙子在不同比例下的最小流化速度,结果表明形状规则生物质／惰性颗粒混合物最小流化速度可由Ｃｈｉｂａ公式计算     

生物质和惰性颗粒二级分混合物的最小流化速度

实验发现纯锯末不能在流化床中流化,锯末中加入惰性固体颗粒构成锯末／玻璃珠、锯末／沙子双组分混合物可实现流化。研究了不同粒径、不同配要比混合物的流化规律。已有的预测双组分混合物最小流化速度的公式不能应用于锯末／玻璃珠／锯末／沙子双组分混合物。     

振动流化床临界流化速度理论预测与实验研究

根据振动流化床床层动力学特性,提出了第一、第二流化段,第一、第二临界流化速度的概念.从流化床临界流化速度的定义出发,得出了振动流化床第一临界流化速度数学模型.在二维振动流化床内,以不同粒径的玻璃珠为床料进行实验研究,分析了振动和其它操作条件对临界流化速度的影响,通过实验数据关联得到了振动能量传递系数的数学表达式,并将模型预测与实验结果进行了对比.结果表明振动强度增加,第一、第二临界速度均减小,振动对第一临界流化速度影响更显著,当振动强度超过1.57后,不通气体床层也能流化,第一临界流化速度降为零,模型预测与实验结果有较好的一致性.     

基于机器学习的气固流化床最小流化速度预测

气固流化床以其高效、可灵活操作等优点广泛应用于煤化工、煤燃烧和煤炭分选等领域。最小流化速度作为气固流化床最主要的操作参数之一,与流化床的操作设计紧密相关。现有的最小流化速度预测模型大多为经验或半经验公式,其准确性和便捷性还存在较大问题。为准确预测气固流化床最小流化速度,基于机器学习建立气固流化床最小流化速度预测模型,并探索模型的内部信息。从颗粒性质与设备条件等方面综合考虑,研究气固流化床的最小流化速度,以系统评估对最小流化速度的综合影响。采用随机森林模型验证了其预测最小流化速度的可行性,并考察了设备参数、颗粒密度和颗粒粒度3个影响因素在预测最小流化速度时的相对重要性。结果表明,最小流化速度与颗粒粒径、颗粒密度和床体直径均呈正相关,皮尔逊相关系数分别为0.79、0.31、0.14,颗粒粒径与最小流化速度相关性最强。随机森林能够根据颗粒性质(密度、粒度)与床体直径准确预测最小流化速度,模型的决定系数达到0.875。特征相关性分析揭示了各特征因素对目标变量的影响方式,颗粒粒度与最小流化速度相关性最强,为预测气固流化床最小流化速度提供借鉴。     

大颗粒振动流化床颗粒临界流化速度研究

建立了带内置水平管的振动流化床,并以颗粒状磷酸一铵为实验物料对带内置水平管的大颗粒振动流化床颗粒临界流化速度进行了实验研究。结果表明,振动的引入对颗粒临界流化速度有着明显的降低作用;同样床层条件下振动频率和振幅越大,床层临界流化速度越低;振动对小粒径颗粒的影响稍强于大粒径颗粒;由实验数据拟合出用于预测该类流化床临界流化速度的经验公式,公式与实验数据吻合良好。     

纳米硅粉颗粒粒径的测试方法研究

文章以高纯度纳米硅粉为研究对象,研究了三种常用纳米颗粒粒径检测方法(激光粒度仪法、比表面积法、X射线衍射法)的检测结果。实验结果表明:与激光粒度仪法相比,BET法与X射线衍射法的测量结果都偏小。     

大差异双组分混合颗粒的最小流化特性

在一套f260 mm′2000 mm的有机玻璃实验装置中,对大差异双组分混合颗粒的最小流化特性进行了实验研究,得到了混合颗粒的流化曲线,由此给出了其起始流化速度、最小流化速度、临界分离速度、完全流化速度等特征速度. 实验结果表明,流化过程可分为4个阶段,即完全流化、大小颗粒分离、大颗粒静止小颗粒流化、固定床阶段,对应混合颗粒的3个状态：完全混合、部分混合部分分离、完全分离状态;混合颗粒的特征速度随小颗粒质量分率的增加而减小,且在小颗粒质量分率达到0.4~0.5后其减小的趋势减缓;混合颗粒的固定床阶段和完全流化阶段的床层空隙率及混合颗粒的体积收缩比在小颗粒质量分率为0.4时达到极值.     

内置水平管的振动流化床中混合物的临界流化速度

在内置水平管的矩形振动流化床内研究了玉米粒和塑料珠混合物(Geldart D颗粒)中的流体力学性质。考察了颗粒质量分数、振动频率、振幅和内置水平管对混合颗粒的临界流化速度的影响。实验表明,振动的加入降低了混合物的临界流化速度。根据振动流化床的动力学特性,分析床层中颗粒受力情况,建立了混合颗粒物在振动流化床中临界流化速度的两类数学模型。通过实验数据建立两种模型的关联式所得的计算值和实验值误差控制在10%以内,吻合较好。     

振动流化床中双组分颗粒流化特性的研究

本文研究了内径为１４８ｍｍ振动圆柱床中等密度和不等密度的双组分颗粒流化特性,考察了不同振动强度对双组分颗粒的床层空隙率、最小流化速度及相图的影响,给出了床层空隙率和最小流化速度的计算式,此计算值与实验值基本相符,且对振动流化床的实际操作和工程设计起到一定的指导作用。     

A Method to Predict the Minimum Fluidization Velocity of Binary Mixtures Based on Particle Packing Properties

An approach was made to predict the minimum fluidization velocity for binary mixtures of spherical particles differing in size and/or density. The spherical multiparticle model proposed by Panigrahi and Murty was employed to describe the relationship between the bed pressure drop and the gas velocity; the voidage at minimum fluidization was estimated by the Westman equation, which was originally used to calculate the packing voidage of mixtures. The predictions agree fairly well with the reported experimental data in the range of Re = 0.12 ? 156, covering both the regions of laminar flow and intermediate flow.     

Fluidization Characteristics of Silicon Particles with a Wide Size Distribution

Fluidization characteristics of silicon particle system are studied by the pressure fluctuation method. The existence of fine particles in the system can improve fluidization. Silicon particles with a wide size distribution, preferably with some fines, behave as Group A particles according to Geldart classification, although the system belongs to Group B actually. The system is also approved to be suitable for organochlorosilane monomer production using a fluidized bed reactor. Experimental data obtained in this work are important for the design and operation of commercial fluidized bed reactors for the production of organochlorosilane monomers.     

Minimum fluidization velocity of binary mixtures

The fast defluidization method was used to measure the minimum fluidization velocities of binary systems. Based on the experimental data obtained from the published literature and from this work, different correlations used for predicting the minimum fluidization velocities of binary systems were evaluated and compared.     

Prediction of pressure fluctuations and minimum fluidization velocity of binary mixtures of geldart group b particles in bubbling fluidized beds

A simple method was proposed to find the pressure fluctuations of binary systems of Geldart Group B particles under bubbling fluidized bed conditions. The pressure fluctuations of binary systems could be predicted from the pressure fluctuations of the individual particles component which comprised the binary systems for completely mixed and partially mixed systems. The predicted pressure fluctuations could be used to calculate the minimum fluidization velocity of the binary systems. The predicted and experimental values of pressure fluctuations and the minimum fluidization velocity of binary systems were in fairly good agreement.     

Multiphase Particle in Cell Simulations of Fluidized Beds: Studies on Bubble Rise Velocity and Minimum Fluidization Velocity

Multiphase particle in a cell simulation of fluidized beds for different Geldart group particles (A, B and D) were investigated. The minimum fluidization velocities predicted by the Barracuda Virtual Reactor® were compared and contrasted by theoretical predictions with the Wen-Yu model. The optimal drag force coefficients based on the Wen-Yu model for MP-PIC simulations was further evaluated for different particle sizes and densities for Group A catalysts. A neural network model was constructed for the optimal model constants as a function of the explanatory variables for group A catalysts. This work illustrates the importance of choosing the correct value for the drag force coefficients to obtain realistic simulation results for fluidized beds. This work clearly demonstrates that the Barracuda simulation results are very sensitive to the value of the Wen-Yu parameter. Using multiphase particle in cell simulations in conjunction with a neural network model can be directly used in further studies as a rapid tool to get the correct values for realistic drag force coefficients based on the Wen-Yu model.