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Partic. vol. 56 pp. 193-206 (June 2021)
doi: 10.1016/j.partic.2020.09.011

Performance optimization of the elliptically vibrating screen with a hybrid MACO-GBDT algorithm

Zhiquan Chena, Zhanfu Lib, Huihuang Xiac, Xin Tonga,b,*

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    • The sieving process is numerically simulated based on the Discrete Element Method (DEM). • The Gradient Boosting Decision Trees (GBDT) algorithm is introduced for the prediction of sieving results. • A hybrid MACO-GBDT algorithm is proposed for the optimization of sieving performance. • The reliability of MACO-GBDT algorithm is verified by the numerical experiments.


As a typical screening apparatus, the elliptically vibrating screen was extensively employed for the size classification of granular materials. Unremitting efforts have been paid on the improvement of sieving performance, but the optimization problem was still perplexing the researchers due to the complexity of sieving process. In the present paper, the sieving process of elliptically vibrating screen was numerically simulated based on the Discrete Element Method (DEM). The production quality and the processing capacity of vibrating screen were measured by the screening efficiency and the screening time, respectively. The sieving parameters including the length of semi-major axis, the length ratio of two semi-axes, the vibration frequency, the inclination angle, the vibration direction angle and the motion direction of screen deck were investigated. Firstly, the Gradient Boosting Decision Trees (GBDT) algorithm was adopted in the modelling task of screening data. The trained prediction models with sufficient generalization performance were obtained, and the relative importance of six parameters for both the screening indexes was revealed. After that, a hybrid MACO-GBDT algorithm based on the Ant Colony Optimization (ACO) was proposed for optimizing the sieving performance of vibrating screen. Both the single objective optimization of screening efficiency and the stepwise optimization of screening results were conducted. Ultimately, the reliability of the MACO-GBDT algorithm were examined by the numerical experiments. The optimization strategy provided in this work would be helpful for the parameter design and the performance improvement of vibrating screens.

Graphical abstract


Discrete Element Method (DEM); Elliptically vibrating screen; Sieving performance; Gradient Boosting Decision Trees (GBDT); Ant Colony Optimization (ACO) algorithm