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СпутникиРНSpaceXРоскосмосNASAIn this work, the fine-grained mismatch rate of wet coal particles on a cross-screen was investigated based on a validated DEM model. The effect of key operating parameters on the fine-grained mismatch rate and a prediction model for the fine mismatch rate were investigated. The results show that the fine-grained mismatch rate could be significantly reduced by decreasing cohesion energy density and the feeding rate, or increasing the rotational speed of roller shafts and screen surface inclination. The genetic algorithms optimised back propagation neural network model predicts the fine-grained mismatch rate better, with the coefficient of determination improved by 3.5%, and the mean squared error and mean absolute error reduced by 65.87% and 48.71%, respectively. This study provides a theoretical basis for the optimisation and intelligent design of cross-screens.
In this work, the fine-grained mismatch rate of wet coal particles on a cross-screen was investigated based on a validated DEM model. The effect of key operating parameters on the fine-grained mismatch rate and a prediction model for the fine mismatch rate were investigated. The results show that the fine-grained mismatch rate could be significantly reduced by decreasing cohesion energy density and the feeding rate, or increasing the rotational speed of roller shafts and screen surface inclination. The genetic algorithms optimised back propagation neural network model predicts the fine-grained mismatch rate better, with the coefficient of determination improved by 3.5%, and the mean squared error and mean absolute error reduced by 65.87% and 48.71%, respectively. This study provides a theoretical basis for the optimisation and intelligent design of cross-screens.
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This research work is generously supported by grants from the National Natural Science Foundation of China (Grant Nos. 52075535, 52125403, 52261135540), as well as the Priority Academic Program Development of Jiangsu Higher Education Institutions.
School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China
Chenhao Guo, Lala Zhao & Feng Xu
School of Chemical Engineering and Technology, China University of Mining and Technology, Xuzhou, 221116, China
Haishen Jiang & Chenlong Duan
Authors
Correspondence to Lala Zhao.
School of Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
Jianrong Tan
School of Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
Zhenyu Liu
Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
Weifei Hu
© 2027 The Chinese Mechanical Engineering Society
Guo, C., Zhao, L., Xu, F., Jiang, H., Duan, C. (2027). Intelligent Prediction of Fine-Grained Mismatch Rate in Cross-Screen Based on Machine Learning Model. In: Tan, J., Liu, Z., Hu, W. (eds) Advances in Mechanical Design. ICMD 2025. Mechanisms and Machine Science, vol 206. Springer, Singapore. https://doi.org/10.1007/978-981-95-7904-4_1
Download citationDOI: https://doi.org/10.1007/978-981-95-7904-4_1
Published: 25 June 2026
Publisher Name: Springer, Singapore
Print ISBN: 978-981-95-7903-7
Online ISBN: 978-981-95-7904-4
eBook Packages: Mechanical Engineering (R0)Springer Nature Proceedings excluding Computer Science