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 performanc...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.展开更多
基金The research work is financially supported by National Natural Science Foundation of China(No.51775113)Natural Science Foundation of Fujian Province(No.2017J01675)+2 种基金51st Scientific Research Fund Program of Fujian University of Technology(No.GY-Z160139)Key Research Platform of NC Equipment and Technology in Fujian Province(No.2014H2002)Subsidized Project for Postgraduates’Innovative Fund in Scientific Research of Huaqiao University(No.17013080007).
文摘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.
