Intelligent Prediction of Sieving Efficiency in Vibrating Screens

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%.

[1]  E Jiaqiang,et al.  Research on the Vibration Characteristics of the New Type of Passive Super Static Vibratory Platform Based on the Multiobjective Parameter Optimization , 2015 .

[2]  Israel J. Lin,et al.  Efficiency of solid particle screening as a function of screen slot size, particle size, and duration of screening. The theoretical approach , 1998 .

[3]  Xin Tong,et al.  Modeling screening efficiency with vibrational parameters based on DEM 3D simulation , 2010 .

[4]  Xibing Li,et al.  Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers , 2013 .

[5]  Ming Hu Zhang,et al.  Intelligent Fault Diagnosis Means and its Application , 2013 .

[6]  Xibing Li,et al.  Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification , 2014 .

[7]  Yasuo Sugai,et al.  A precipitation estimation system based on support vector machine and neural network , 2006 .

[8]  Zhao Yuemin,et al.  Screen Simulation Using a Particle Discrete Element Method , 2007 .

[9]  Anthony Kuh,et al.  Comments on "Pruning Error Minimization in Least Squares Support Vector Machines" , 2007, IEEE Trans. Neural Networks.

[10]  E Jiaqiang,et al.  Intelligent fitting of minimum spout-fluidised velocity in spout-fluidised bed , 2011 .

[11]  Konstantinos P. Ferentinos,et al.  Adaptive design optimization of wireless sensor networks using genetic algorithms , 2007, Comput. Networks.

[12]  E Jiaqiang,et al.  Design of the H∞ robust control for the piezoelectric actuator based on chaos optimization algorithm , 2015 .

[13]  E Jiaqiang,et al.  Chaos Analysis on the Acceleration Control Signals of the Piezoelectric Actuators in the Stewart Platform , 2016 .

[14]  Kejun Dong,et al.  DEM simulation of particle flow on a multi-deck banana screen , 2009 .

[15]  Jiao Hong-guang.  Test and Research on Optimum Configuration of Diameter of Screen Aperture and Incline of Screen Deck , 2007 .

[16]  Li Qiyue,et al.  Comparisons of Random Forest and Support Vector Machine for Predicting Blasting Vibration Characteristic Parameters , 2011 .

[17]  R. T. Walczak,et al.  Using Support Vector Machines to Develop Pedotransfer Functions for Water Retention of Soils in Poland , 2008 .

[18]  E Jiaqiang,et al.  Prediction of jet penetration depth based on least square support vector machine , 2010 .

[19]  Keshun Liu,et al.  Some factors affecting sieving performance and efficiency , 2009 .

[20]  Paul W. Cleary,et al.  Testing the validity of the spherical DEM model in simulating real granular screening processes , 2012 .

[21]  Colin Webb,et al.  Discrete particle motion on sieves—a numerical study using the DEM simulation , 2003 .