摘要
基于截面200 mm×20 mm,高1600 mm,锥角60°的矩形喷动流化床,以二组分混合颗粒、单一组分球形颗粒及非球形颗粒为物料进行最大喷动压降的实验研究。结果表明,最大喷动压降随静止床高、颗粒密度、颗粒球形度及二组分混合颗粒体系中沉积组分分率增加而增大,随流化气速增大而减小;增大颗粒粒径或喷口宽度,呈现先减小后增大趋势,存在极小值;对于非球形颗粒及混合颗粒,其床层总压降随表观喷动气速变化的规律与单一组分球形颗粒相同,但相同条件下的前者压降波动幅度大于后者。以实验测量的854组数据为基础,结合量纲分析法建立了最大喷动压降预测关联式,与测量值及文献实验数据吻合较好。
The maximum spouting pressure drop(Δpm)is one of the important parameters for the design and operation of the spout-fluid bed.Almost all the literature correlations for predicting theΔpm were developed from single-component particles or spherical particles,and hence were with some limitation in actual application.In view of this,with binary particle mixtures,single-component spherical particles and non-spherical particles as experimental materials,theΔpm was studied based on a rectangular spout-fluid bed with cross section of 200 mm×20 mm,height of 1600 mm and conical base angle of 60°.The results showed that theΔpm increased with the static bed height,particle density,particle sphericity and deposition component ratio,and decreased with the fluidized gas velocity.While with the increase in particle size or spout nozzle size,it went down firstly and then up with a minimum pressure drop.For non-spherical particles and binary particle mixtures,the variation of total pressure drop with superficial spouting gas velocities was found to be similar with that of single-component spherical particles,while the fluctuation range of total pressure drop was larger under the same conditions.Based on the 854 sets of measured data by experiments,a predicting correlation of theΔpm was proposed by dimensional analysis method,which was in good agreement with the measured data in this work and the experimental data in literature.
作者
付爽
王东祥
俞建峰
金海安
Shuang FU;Dongxiang WANG;Jianfeng YU;Hai'an JIN(School of Mechanical Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China;Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment&Technology,Jiangnan University,Wuxi,Jiangsu 214122,China)
出处
《过程工程学报》
CAS
CSCD
北大核心
2021年第8期918-925,共8页
The Chinese Journal of Process Engineering
基金
国家自然科学基金资助项目(编号:51606086)
中国博士后科学基金项目(编号:2017M621624)。
关键词
喷动流化床
最大喷动压降
二组分混合颗粒
非球形颗粒
预测关联式
spout-fluid bed
maximum spouting pressure drop
binary particle mixtures
non-spherical particles
prediction correlation