Intelligent Prediction of Sieving Efficiency in Vibrating Screens
Joint Authors
Zhang, Bin
Gong, Jinke
Yuan, Wenhua
Fu, Jun
Huang, Yi
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-05-11
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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%.
American Psychological Association (APA)
Zhang, Bin& Gong, Jinke& Yuan, Wenhua& Fu, Jun& Huang, Yi. 2016. Intelligent Prediction of Sieving Efficiency in Vibrating Screens. Shock and Vibration،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1120040
Modern Language Association (MLA)
Zhang, Bin…[et al.]. Intelligent Prediction of Sieving Efficiency in Vibrating Screens. Shock and Vibration No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1120040
American Medical Association (AMA)
Zhang, Bin& Gong, Jinke& Yuan, Wenhua& Fu, Jun& Huang, Yi. Intelligent Prediction of Sieving Efficiency in Vibrating Screens. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1120040
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1120040