Intelligent Prediction of Sieving Efficiency in Vibrating Screens

• Main Authors: Bin Zhang, Jinke Gong, Wenhua Yuan, Jun Fu, Yi Huang
• Format: Article
• Language: English
• Published: Hindawi Limited 2016-01-01
• Series: Shock and Vibration
• Online Access: http://dx.doi.org/10.1155/2016/9175417

In order to effectively predict the sieving efficiency of a vibrating screen, experiments to investigate the sieving efficiency were carried out. Relation between sieving efficiency and other working parameters in a vibrating screen such as mesh aperture size, screen length, inclination angle, vibration amplitude, and vibration frequency was analyzed. Based on the experiments, least square support vector machine (LS-SVM) was established to predict the sieving efficiency, and adaptive genetic algorithm and cross-validation algorithm were used to optimize the parameters in LS-SVM. By the examination of testing points, the prediction performance of least square support vector machine is better than that of the existing formula and neural network, and its average relative error is only 4.2%.