Research on cross-screening mechanism of moist coal particles and intelligent prediction method of screening performance.
Saved in:
| Title: | Research on cross-screening mechanism of moist coal particles and intelligent prediction method of screening performance. |
|---|---|
| Authors: | Guo, Chenhao1 (AUTHOR), Zhao, Lala1,2 (AUTHOR) lala.zhao@cumt.edu.cn, Xu, Feng1 (AUTHOR), Duan, Chenlong2,3 (AUTHOR), Jiang, Haishen2,3 (AUTHOR), Yang, Yadong2,4 (AUTHOR), Liu, Zeping2,4 (AUTHOR) |
| Source: | International Journal of Coal Preparation & Utilization. 2026, Vol. 46 Issue 5, p1193-1213. 21p. |
| Subject Terms: | *Discrete element method, *Machine learning, *Process optimization, *Support vector machines, *Coal dust, *Particle swarm optimization |
| Abstract: | In this work, the cross-screening mechanism of moist coal particles on the cross-screen was investigated based on DEM (discrete element method) and a validated linear cohesion model. The impact of various operating parameters on the screening performance of cross-screen were explored. Three machine learning models were established to forecast the screening performance of cross-screen. The results show that significant improvements in screening efficiency η can be achieved by reducing the feeding rate q and cohesion energy density k, or increasing the rotational speed of roller shafts n and the inclination angles of the screen surface θ. The fluctuation of the average particle velocity v is negligible at different q. The PSO -SVM (particle swarm optimization and support vector machine) prediction model emerged the best predictive performance for screening performance with superior data fitting. This work provides crucial theoretical understandings for optimization and intelligent design of cross-screen. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
