新型螺旋筛面旋振筛的参数优化

Parameters Optimization of Spin-vibrating Screen with Spiral Screen Surface

为研究螺旋筛面旋振筛的筛分效率与筛分参数之间的非线性关系,文章使用神经网络对其筛分参数综合寻优,得到最佳筛分参数及筛分效率。首先,建立不同结构参数的螺旋筛面旋振筛三维模型,利用离散单元法对其仿真模拟,获取不同参数下的筛分效率数据。然后,利用神经网络对样本数据进行训练、验证及得到预测数据。研究结果表明:通过神经网络的训练和测试,证明神经网络可以用于螺旋筛面旋振筛的参数优化;在螺旋升角为11.613°、振动频率为15.720 Hz、振动幅度为1.481 mm、螺旋圈数为1.468圈、内外径比值为0.256时,螺旋筛面旋振筛获得最佳筛分效率,并且优化了螺旋圈数,缩短了筛长。

In order to study the nonlinear relationship between the screening efficiency and screening parameters of spin-vibrating screen with spiral screen surface,the article uses neural network to comprehensively find the optimization of its screening parameters,and get the best screening parameters and screening efficiency.First,the three-dimensional model of spin-vibrating screen with spiral screen surface with different structural parameters is established,and the Discrete Element Method is used to simulate it and obtain the screening efficiency data under different parameters.Then,a neural network is used to train,verify and predict the sample data.The results show that:through the training and testing of neural network,it is proved that the neural network can be used for the parameter optimization of spin-vibrating screen with spiral screen surface;when the spiral rise angle is 11.613°,the vibration frequency 15.720Hz,the vibration amplitude 1.481mm,the number of spiral circles 1.468 circles,and the ratio of the inner and outer diameters 0.256,the spin-vibrating screen with spiral screen surface obtains the optimum screening efficiency,and the number of spiral circles and the screening time are shortened.