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Volume 114
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Screening efficiency and energy consumption collaborative optimization of linear vibrating screen using coupled DEM-MBK method
Weimin Jing a b 1, Tong Wang c d 1, Shida Zhao b, Xuguo Yan b, Huan Zhang b *
a College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, China
b Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, China
c School of Mechanical Engineering, Wuhan Vocational College of Software and Engineering, Wuhan, 430205, China
d Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts, Wuhan, 430205, China
10.1016/j.partic.2026.04.016
Volume 114, July 2026, Pages 283-292
Received 27 January 2026, Revised 10 April 2026, Accepted 21 April 2026, Available online 2 May 2026, Version of Record 12 May 2026.
E-mail: zhanghuan@wust.edu.cn
Highlights

• A bidirectional DEM–MBK coupled model is developed for a linear vibrating screen.

• Mechanical Energy Consumption (MEC) and Screening Efficiency (SE) are evaluated.

• Effects of four key screening parameters on SE and MEC are analyzed.

• Dual-objective optimizations of SE and MEC are performed to identify the MEC space.

• High SE & low MEC can be achieved through appropriate parameter combinations.


Abstract

Vibrating screens are essential in mineral processing, operating continuously under heavy loads and consuming considerable energy. Reducing mechanical energy consumption (MEC) while maintaining high screening efficiency (SE) is an important engineering challenge. This study develops a collaborative optimization framework for SE and MEC using bidirectional Discrete Element Method–Multi-Body Kinematics (DEM–MBK) simulations and virtual experiments. A DEM–MBK model of a CWKS1218 linear vibrating screen is established to capture the interaction between particle motion and screen dynamics driven by the excitation source. MEC is quantified as the time-averaged input power of the exciter at steady state. A Central Composite Circumscribed (CCC) design is used to construct response surface models and analyze the effects of excitation force, screen inclination, vibration frequency, and vibration direction angle on SE and MEC. Based on the fitted models, Non-dominated Sorting Genetic Algorithm II performs dual-objective optimization. The results reveal a clear trade-off between SE and MEC: in the low-MEC regime, improving SE requires increased energy input. Below 98.12% SE, different parameter combinations produce a wide energy-consumption range, whereas at 98.12% SE, Pareto-optimal solutions converge to a unique parameter setting. Additional simulations validate the optimization results, demonstrating acceptable prediction accuracy.

Graphical abstract
Keywords
DEM–MBK; Screening efficiency; Mechanical energy consumption; Collaborative optimization; Screening parameters combination
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