气固分离过程中,颗粒碰撞壁面的反弹行为对颗粒运动及装置的分离效率将产生重要影响。已有研究多针对球形颗粒的碰撞行为,然而实际工业过程中,煤粉、生物质、矿石等颗粒均为非球形颗粒,该类颗粒与壁面碰撞后的反弹行为与球形颗粒存在显著差异。为深入掌握非球形颗粒碰撞壁面的反弹行为,搭建了颗粒壁面碰撞试验装置,采用高速摄像及图像处理方法获得典型非球形颗粒碰撞壁面的基础数据,分析颗粒材质、球形度、壁面粗糙度、撞击角度、撞击速度等关键参数对颗粒-壁面反弹行为的影响。基于颗粒壁面碰撞四参数模型和神经网络模型分别预测了非球形颗粒与壁面的反弹行为的统计和随机分布特性。结果表明各类非球形颗粒与粗糙壁面碰撞的反弹行为存在一致性,说明球形度低于一定数值后,将成为颗粒壁面碰撞反弹行为的主导因素。四参数模型能很好地预测碰撞结果及随机分布特征,而基于试验数据训练的神经网络模型可达到更好的预测效果。

The particle rebound behaviors of particle-wall collisions have significant impacts on the particle motion and the separation efficiency in the gas-solid separation process. Previous studies have focused on the collision behavior of the spherical particles. However, inactual industrial processes, particles such as coal powder, biomass, and ore are all non-spherical particles. There are significant differences in the rebound behavior between the non-spherical particles colliding with the wall and the spherical particles. To explore the rebound behavior of the non-spherical particles colliding with the wall, an experimental device for particle-wall collisions was established.High-speed photography and image processing methods were used to obtain basic data of particle-wall collisions of the non-spherical particles. The influence of the key parameters such as particle material, sphericity, wall roughness, impact angle, and impact speed on particle-wall rebound behavior was analyzed. Based on the established four - parameter model of particle - wall collisions and neural network models, the rebound behavior between non-spherical particles and the wall was predicted. The results indicate that there is consistency in the rebound behavior of non-spherical particles colliding with the wall. Sphericity plays an important role in particle-wall collisions. The four-parameter model can predict collision results and random distribution characteristics well, while neural network modelstrained based on experimental data can achieve better prediction results.

0 引言

1 颗粒壁面碰撞试验

   1.1 碰撞过程描述

   1.2 试验系统描述

2 试验结果和分析

   2.1 典型非球形颗粒碰撞壁面行为

   2.2 四参数模型

   2.3 神经网络模型

3 结论