Performance of large-difference-particle air classifier

doi:10.11949/0438-1157.20201807

Abstract

Abstract:

As a fundamental industrial operation, the granular air classification is widely used in mineral processing, filter material regeneration, powder cleaning and other industrial areas. However, most of the static air classifiers cannot take into account industrial requirements such as high particle dispersion, low pressure drop, easy operation, and regular flow field. And relevant reports are based on particle mixture with better fluidization properties. There is no research on particle mixture which is difficult to fluidize and has large differences on diameter. This research proposes a new type of large-difference-particle air classifier, which enables the mixture to be fully dispersed, while regulating the flow field and controlling the pressure drop. The specific situation is as follows: a cone Johnson screen internal with high opening rate and smooth surface are arranged in the classification chamber to disperse the materials. Meanwhile, a frustum-shaped Johnson screen is set at the outlet of large particles to rectify the gas and realize dust removal, which can enhance the effect of air flow on the separation of particles. Microsilica powder is used as dust and molecular sieve adsorbent as the large particle. This study investigated the classification characteristics of novel air classifier before and after setting the dispersed Johnson screen. Two important classification parameters (the pressure drop and the classification efficiency) were explored under different operating parameters including the superficial gas velocity U, the granular circulation rate W and the dust/large particle ratio R. It is found that in a free bed, with an increase of the superficial gas velocity, the pressure drop of the equipment increases simultaneously, and the classification efficiency reaches a maximal value 87% when U=0.27 m/s. The influence of granular circulation rate on the pressure drop is not so clear, but the classification efficiency decreases continuously with an increase of W. When the dust/large particle ratio R is relatively low, the pressure drop does not change obviously. As it exceeds the tolerance of the Johnson screen, however, the dust deposited in the Johnson screen forms an ash layer, which results in an exponential increase in pressure drop and a sharp decline in efficiency. Compared to the free bed, setting a dispersed Johnson screen, the equipment pressure drop and classification efficiency are not sensitive to the variation of the operating parameters due to its rectification and distribution function. The dispersed Johnson screen extends the operating dust/large particle ratios whereas limits the superficial gas velocity. The actual pressure drop of the equipment is divided into three parts: Johnson network pressure drop, particle friction pressure drop, and gas outlet pressure drop. Based on the experimental results, a model for calculating the pressure drop is given.

Key words: granular materials, separation, process control, air classification, Johnson screen

摘要：

通过改变表观气速U、颗粒循环速率W、粉尘/捕集颗粒比R等操作参数，考察了大差异颗粒空气分级设备在设置内构件前后的压降和分级效率的变化。结果表明，自由床时，压降随表观气速的增大而增大，分离效率在U=0.27 m/s时达到最大值87%。捕集颗粒循环量对压降的影响较小，分级效率随W的增大而持续下降。粉尘/捕集颗粒比较低时，压降无变化，但增大至超过约翰逊网的阻塞限度后，操作压降呈指数型增长，分级效率迅速下降。设置内构件后，由于其起到了整流和分布作用，设备压降和分级效率的变化不如自由床时敏感，拓宽了可操作的粉尘/捕集颗粒比范围，但缩小了可操作的表观气速范围。将设备实际压降划分为约翰逊网压降、颗粒摩擦压降、气体出口压降三个部分，基于实验结果，给出了计算压降的模型。

关键词: 颗粒物料, 分离, 过程控制, 空气分级, 约翰逊网

CLC Number:

TQ 028.9

Daoming LU, Zhaoting TANG, Yiping FAN, Chunxi LU. Performance of large-difference-particle air classifier[J]. CIESC Journal, 2021, 72(8): 4184-4195.

卢道铭, 唐钊艇, 范怡平, 卢春喜. 大差异颗粒分级再生设备的性能研究[J]. 化工学报, 2021, 72(8): 4184-4195.

Figures/Tables 16

Fig.1 Large-difference-particle air classifier

Fig.2 Particle size distribution of the microsilica

Table 1 Physical properties of large granules

材料

材料密度/

(kg/m³)

颗粒密度/

(kg/m³)

堆密度/

(kg/m³)

d₅₀

Fig.3 Fluctuation of static pressure drop with time under different superficial gas velocity

Fig.4 Influence of diversion effect of dispersed Johnson screen on feeding

Fig.5 Variation of static pressure drop under different granular circulation rate

Fig.6 Variation of two type beds pressure drop with time under different conditions

Table 2 Pressure drop of free bed（W=0.19 kg/s）

Q/(m³/h)	U/(m/s)	Δp/Pa	F_j1/Pa	F_o/Pa	F_p/Pa
200	0.18	57	24	11	21
250	0.23	70	32	14	23
300	0.27	88	44	18	25
350	0.32	110	59	25	27
400	0.37	144	79	35	30
450	0.41	171	95	42	34

Table 3 Pressure drop of internals bed（W=0.19 kg/s）

Q/(m³/h)	U/(m/s)	Δp/Pa	F_j1/Pa	F_j2/Pa	F_o/Pa	F_p/Pa
200	0.18	60	24	4	11	21
250	0.23	69	32	4	13	20
300	0.27	89	44	6	18	20

Table 4 Summary of correlation for pressure drop coefficient （ξ） in orifices plate

文献	方程	ξ_j1	ξ_j2
[26]	$ξ = 0.67 β - 4.48$	27.2848	2.1745
[27]	$ξ = 1 - ψ 2 C 0 ψ 2$ C₀为微孔系数，与孔间距P有关，查图可得	47.5489	2.6121
[28]	$ξ = ξ 0 t k 2.9 - 3.79 t d β 0.4 + 1.79 t d 2 β 0.8$ $ξ 0 t k = 1 - 2 β 2 + 2 β 4 1 - 1 C c + 1 2 C c 2$ $C c = 0.72$	22.8767	0.8509
[29]	$ξ = C 0 (1 - C c β 2) 2 C c 2 β 4$ $C c = 0.596 + 0.0031 e β / 0.206$ $C 0 = 0.5 + 0.178 4 (t / d) 2 + 0.355$	27.7078	0.9285

Table 4 Summary of correlation for pressure drop coefficient （ξ） in orifices plate

文献	方程	ξ_j1	ξ_j2
[26]	$ξ = 0.67 β - 4.48$	27.2848	2.1745
[27]	$ξ = 1 - ψ 2 C 0 ψ 2$ C₀为微孔系数，与孔间距P有关，查图可得	47.5489	2.6121
[28]	$ξ = ξ 0 t k 2.9 - 3.79 t d β 0.4 + 1.79 t d 2 β 0.8$ $ξ 0 t k = 1 - 2 β 2 + 2 β 4 1 - 1 C c + 1 2 C c 2$ $C c = 0.72$	22.8767	0.8509
[29]	$ξ = C 0 (1 - C c β 2) 2 C c 2 β 4$ $C c = 0.596 + 0.0031 e β / 0.206$ $C 0 = 0.5 + 0.178 4 (t / d) 2 + 0.355$	27.7078	0.9285

Fig.7 Comparison between predicted and experimental values of pressure drop in Johnson screen for collcetion

Fig.8 Comparison between predicted and experimental values of pressure drop in outlet

Fig.9 Comparison of experimental and fitting results of ξ0

Fig.10 Comparison of experimental and Ergun fitting results

Fig.11 Variation of classification efficiency under different operation conditions

Fig.12 Variation of dust diameter under different operation conditions

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