# Dynamic Coal Flow-Based Energy Consumption Optimization of Scraper Conveyor
## 6 Conclusions  
This study focuses on optimizing the energy efficiency of coal mining equipment, with a primary focus on developing dynamic coal flow and scraper conveyor energy efficiency models. The effects of key variables on energy consumption and the block coal rate were analyzed, leading to the following conclusions:  
1. Through theoretical analysis and EDEM simulations, a reliable model for describing coal flow dynamics was developed. The comparison of simulation results with real-world data confirms the model's reliability, with errors within an acceptable range. This provides a strong foundation for accurately describing coal flow distribution and trends.  
2. A multi-objective optimization approach was applied to reduce scraper conveyor energy consumption while managing the size of lump coal. The selected compromise solution resulted in a 33.7% reduction in energy consumption (from 1.9034 \(\times\) 10\({}^{9}\) KJ to 1.2617 \(\times\) 10\({}^{9}\) KJ) and a 64.5% decrease in lump coal area (from 13,827 mm\({}^{2}\) to 4912 mm\({}^{2}\)). These results reflect a trade-off between energy efficiency and coal fragmentation, indicating that significant energy savings can be achieved while maintaining an acceptable lump coal size distribution.  
3. This study found that the traction speed, scraper chain speed, and drum rotational speed are key factors affecting energy consumption and the block coal rate. Lowering the traction speed while increasing the chain speed and drum speed results in higher energy consumption, while optimizing these parameters can reduce energy use. Additionally, a proper balance between traction speed and drum speed is critical for improving block coal rate, with both parameters showing significant improvements when optimized together.  
Although the proposed model effectively captures the dynamics of coal flow and energy consumption in fully mechanized mining systems, it still has limitations in terms of adaptability. Specifically, the current optimization framework has not been fully validated under extreme operating conditions such as high humidity, low temperatures, or complex geological environments. In real-world applications across different mines, variations in geological and environmental conditions may require customized parameter tuning to ensure model accuracy and applicability. Future research will focus on enhancing model robustness, expanding its adaptability to diverse mining scenarios, and integrating real-time monitoring and feedback mechanisms to further improve energy optimization performance.  
Conceptualization, J.L.; Validation, Q.L.; Investigation, Q.M.; Resources, X.C.; Data curation, T.X.; Writing--original draft, Y.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Scientific Research Program of the Shaanxi Provincial Department of Education-Youth Innovation Team Project, the National Natural Science Foundation of China (24 JF111), Shaanxi Province Natural Science Basic Research Program (2025 JC-YBMS-543), the National Natural Science Foundation of China (52005400), Innovative Research Group Project of the National Natural Science Foundation of China (52274158), and the Key Research and Development Projects of Shaanxi Province (2024 QY2-GJHX-09).  
Not applicable.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Yuandatan Coal Mine. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.