A Method of Ore Blending Based on the Quality of Beneficiation and Its Application in a Concentrator
Source abstract
Abstract
Ore blending is an essential part of daily work in the concentrator. Qualified ore dressing products can make the ore dressing more smoothly. The existing ore blending modeling usually only considers the quality of ore blending products and ignores the effect of ore blending on ore dressing. This research proposes an ore blending modeling method based on the quality of the beneficiation concentrate. The relationship between the properties of ore blending products and the total concentrate recovery is fitted by the ABC-BP neural network algorithm, taken as the optimization goal to guarantee the quality of ore dressing products at the source. The ore blending system was developed and operated stably on the production site. The industrial test and actual production results have proved the effectiveness and reliability of this method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Mining Techniques and Economics · Mineral Processing and Grinding · Minerals Flotation and Separation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Funding
National Natural Science Foundation of China
References (30)
Show source-provided references
1. Nava, J.V., Llorens, T., and Menéndez-Aguado, J.M. (2020). Kinetics of Dry-Batch Grinding in a Laboratory-Scale Ball Mill of Sn–Ta–Nb Minerals from the Penouta Mine (Spain). Metals, 10.10.3390/met10121687 ↗
2. The effect of ore blends on the mineral processing of platinum ores10.1016/j.mineng.2010.02.008 ↗
3. A diversified Tabu search approach for the open-pit mine production scheduling problem with metal uncertainty10.1016/j.ejor.2012.05.029 ↗
4. Algorithmic approach to pushback design based on stochastic programming: Method, application and comparisons10.1179/037178410X12780655704761 ↗
5. Production scheduling with uncertain supply: A new solution to the open pit mining problem10.1007/s11081-012-9186-2 ↗
6. Stochastic long-term production scheduling of iron ore deposits: Integrating joint multi-element geological uncertainty10.1134/S1062739149010097 ↗
7. Stochastic optimisation model for open pit mine planning: Application and risk analysis at copper deposit10.1179/174328607X228848 ↗
8. Global optimization of open pit mining complexes with uncertainty10.1016/j.asoc.2015.11.038 ↗
9. Tailored Lagrangian Relaxation for the open pit block sequencing problem10.1007/s10479-012-1287-y ↗
10. LP-based disaggregation approaches to solving the open pit mining production scheduling problem with block processing selectivity10.1016/j.cor.2007.12.006 ↗
11. Algorithmic integration of geological uncertainty in pushback designs for complex multiprocess open pit mines10.1179/147490013X13639459465736 ↗
12. Ferland, J.A., Amaya, J., and Djuimo, M.S. (2007). Application of a Particle Swarm Algorithm to the Capacitated Open Pit Mining Problem, Springer.10.1007/978-3-540-73424-6_15 ↗
13. Long term production planning of open pit mines by ant colony optimization10.1016/j.ejor.2014.07.040 ↗
14. Optimizing ore-waste discrimination and block sequencing through simulated annealing10.1016/j.asoc.2013.03.005 ↗
15. Production Scheduling of Open Pit Mines Using Particle Swarm Optimization Algorithm
16. Stochastic mine production scheduling with multiple processes: Application at Escondida Norte, Chile10.1134/S1062739149040096 ↗
17. A minimum cost network flow model for the maximum covering and patrol routing problem10.1016/j.ejor.2015.05.067 ↗
18. Dominy, S., Connor, L.O., Parbhakar-Fox, A., Glass, H., and Purevgerel, S. (2018). Geometallurgy—A Route to More Resilient Mine Operations. Minerals, 8.10.3390/min8120560 ↗
19. Strategic evaluation of concentrator operational modes under geological uncertainty10.1016/j.minpro.2017.05.009 ↗
20. Grindability of binary ore blends in ball mills10.1016/j.mineng.2012.11.001 ↗
21. Birat, J. (2020). Society, Materials, and the Environment: The Case of Steel. Metals, 10.10.3390/met10030331 ↗
22. Phull, J., Egas, J., Barui, S., Mukherjee, S., and Chattopadhyay, K. (2020). An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking. Metals, 10.10.3390/met10010025 ↗
23. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm10.1007/s10898-007-9149-x ↗
24. A survey: Algorithms simulating bee swarm intelligence10.1007/s10462-009-9127-4 ↗
25. Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm
26. A hybrid simultaneous perturbation artificial bee colony and back-propagation algorithm for training a local linear radial basis neural network on ore grade estimation10.1016/j.neucom.2017.01.016 ↗
27. Approximation of the Piecewise Function Using Neural Fuzzy Networks with an Improved Artificial Bee Colony Algorithm10.12720/joace.4.1.18-21 ↗
28. Anuar, S., Selamat, A., and Sallehuddin, R. (2015). Hybrid artificial neural network with artificial bee colony algorithm for crime classification. Computational Intelligence in Information Systems, Springer.10.1007/978-3-319-13153-5_4 ↗
29. Ghanem, W.A.H.M., and Jantan, A. (2014, January 28–30). Swarm intelligence and neural network for data classification. Proceedings of the 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), Penang, Malaysia.10.1109/ICCSCE.2014.7072714 ↗
30. A smart artificial bee colony algorithm with distance-fitness-based neighbor search and its application10.1016/j.future.2018.06.054 ↗