Author(s)

Bin Li ¹

Affiliation(s)

¹ Yuanbaoshan Open-pit Coal Mine, Inner Mongolia Pingzhuang Coal Industry (Group) Co., Ltd., Chifeng City, 024076, Inner Mongolia, China

Corresponding Author

Bin Li

ABSTRACT

In the process of open-pit mining planning, there are problems such as low mineral resource recovery rate, inaccurate slope stability control, and insufficient production cost optimization. To this end, this paper combines the particle swarm algorithm (PSO) with the BP neural network to improve the level of detail control in open-pit mining planning and optimize the mining plan. First, the particle swarm algorithm is used for preliminary global optimization, and dynamic optimization is performed for key parameters in open-pit mining (such as mining path, stripping ratio, slope angle, etc.) to ensure the rationality of the overall planning. Then, a BP neural network is constructed to train historical data and predict resource recovery rate, slope stability trend and economic cost under different mining schemes. Finally, the global optimization results of the particle swarm algorithm are used as input parameters of the BP neural network to achieve refined control and improve the safety and economy of open-pit mining by iteratively adjusting the optimization scheme. The experiment shows that the PSO optimized BP method performs best in mining efficiency, reaching 145 tons/hour, significantly higher than the other two methods. In contrast, the BP neural network has a mining efficiency of 120 tons/hour, achieving more accurate optimization of open-pit mining planning.

KEYWORDS

Particle Swarm Optimization (PSO); BP Neural Network; Open-pit Mining Planning; Detail Control; Optimization Algorithm

CITE THIS PAPER

Bin Li, Optimization of Open-pit Mining Planning Based on Particle Swarm Optimization and BP Neural Network. Frontiers in Power and Energy Systems (2025) Vol. 4: 21-30. DOI: http://dx.doi.org/10.23977/fpes.2025.040104.

REFERENCES

[1] Shi D, Li Z, Zurada J, et al. Ontology-based text convolution neural network (TextCNN) for prediction of construction accidents[J]. Knowledge and Information Systems, 2024, 66(4): 2651-2681.
[2] Nancel-Penard P, Jelvez E. A direct block scheduling model considering operational space requirement for strategic open-pit mine production planning[J]. Optimization and Engineering, 2024, 25(4): 2149-2175.
[3] Turan G, Onur A H. Optimization of open-pit mine design and production planning with an improved floating cone algorithm[J]. Optimization and Engineering, 2023, 24(2): 1157-1181.
[4] Mirzaei-Nasirabad H, Mohtasham M, Askari-Nasab H, et al. An optimization model for the real-time truck dispatching problem in open-pit mining operations[J]. Optimization and Engineering, 2023, 24(4): 2449-2473.
[5] Gong H, Tabesh M, Moradi Afrapoli A, et al. Near-face stockpile open pit mining: a method to enhance NPV and quality of the plant throughput[J]. International Journal of Mining, Reclamation and Environment, 2023, 37(3): 200-215.
[6] Habib N A, Ben-Awuah E, Askari-Nasab H. Short-term planning of open pit mines with Semi-Mobile IPCC: a shovel allocation model[J]. International Journal of Mining, Reclamation and Environment, 2024, 38(3): 236-266.
[7] Nabavi Z, Mousavi A, Mirzehi Kalate Kazemi M, et al. Incorporating grade uncertainty into open-pit long-term production planning using loss and profit functions[J]. International Journal of Mining and Geo-Engineering, 2024, 58(1): 59-67.
[8] Noriega R, Pourrahimian Y. Shovel allocation and scheduling for open-pit mining using deep reinforcement learning[J]. International Journal of Mining, Reclamation and Environment, 2024, 38(6): 442-459.
[9] Liu S Q, Kozan E, Corry P, et al. A real-world mine excavators timetabling methodology in open-pit mining[J]. Optimization and Engineering, 2023, 24(3): 1493-1535.
[10] Dehghani H, Sakinezhad R, Nabavi Z, et al. Incorporation price uncertainty into open-pit to underground mine transition[J]. Mineral Economics, 2024, 37(1): 89-99.
[11] Azadi N, Mirzaei-Nasirabad H, Mousavi A. Evaluating the efficiency of the genetic algorithm in designing the ultimate pit limit of open-pit mines[J]. International Journal of Mining and Geo-Engineering, 2023, 57(1): 55-58.

Sponsors, Associates, and Links

---

• Journal of Metallurgy and Mineral Processing
  
  
• International Journal of Oil and Gas Science and Engineering
  
  
• Journal of Disaster Prevention and Reduction
  
  
• Smart Systems and Green Energy
  
  
• Journal of Mining Engineering and Safety Technology
  
  
• Thermal Power Engineering
  
  
• Energy Conversion Research