Multi-source and multi-target iron ore blending method in open pit mine

• Autorzy: Yao Jiang , Liu Chunhui , Huang Guichen , Xu Kai , Yuan Qingbo

Identyfikatory

DOI

10.24425/ams.2022.143678

• Języki publikacji: EN

Abstrakty

EN

Iron ore blending in an open-pit mine is an important means to ensure ore grade balance and resource recycling in iron mine industrial production. With the comprehensive recovery and utilisation of resource mining, the multi-source and multi-target ore blending method has become one of the focuses of the mining industry. Scientific and reasonable ore blending can effectively reduce the transportation cost of the enterprise. It can also ensure that the ore grade, washability index and iron carbonate content meet the requirements of the concentrator and significantly improve the comprehensive utilisation rate and economic benefits of the ore. An ore blending method for open-pit iron ore is proposed in this paper. The blending method is realised by establishing the ore blending model. This model aims to achieve maximum ore output and the shortest transportation distance, ore washability index, total iron grade, ferrous iron grade and iron carbonate content after the ore blending meets the requirements. This method can meet the situation of a single mine to a single concentrator and that of a single mine to multiple concentrators. According to the results of ore blending, we can know the bottleneck of current production. Through targeted optimisation management, we can tap the production potential of an open-pit mine.

Słowa kluczowe

EN

iron ore blending; multi-source and multi-target; open pit mine

PL

ruda żelaza; kopalnia odkrywkowa; kopalnia żelaza

• Wydawca: Instytut Mechaniki Górotworu PAN
• Czasopismo: Archives of Mining Sciences
• Rocznik: 2022
• Tom: Vol. 67, no. 4
• Strony: 631--644
• Opis fizyczny: Bibliogr. 31 poz., tab.

Twórcy

autor

Yao Jiang

• Northeastern University, College of Resources and Civil Engineering, Shenyang, Liaoning 110819, China

• https://orcid.org/0000-0002-4034-1817

autor

Liu Chunhui

• ANSTEEL Group Guanbaoshan Mining Co., Ltd, Anshan, Liaoning 114000, China

• https://orcid.org/0000-0002-7444-2739

autor

Huang Guichen

• ANSTEEL Group Guanbaoshan Mining Co., Ltd, Anshan, Liaoning 114000, China

• https://orcid.org/0000-0003-3964-2329

autor

Xu Kai

• ANSTEEL Group Guanbaoshan Mining Co., Ltd, Anshan, Liaoning 114000, China

• https://orcid.org/0000-0003-4434-8407

autor

Yuan Qingbo

• ANSTEEL Group Guanbaoshan Mining Co., Ltd, Anshan, Liaoning 114000, China

• https://orcid.org/0000-0002-2558-006X

Bibliografia

• [1] A. Lamghari, R. Dimitrakopoulos, A diversified Tabu search approach for the open-pit mine production scheduling problem with metal uncertainty. Eur. J. Oper. Res. 222 (3), 642-652 (2012). DOI: https://doi.org/10.1016/j.ejor.2012.05.029.
• [2] F.R.A. Consuegra, R. Dimitrakopoulos, Algorithmic approach to pushback design based on stochastic programming: method, application and comparisons. Mining Technology 119 (2), 88-101 (2010). DOI: https://doi.org/10.1179/037178410X12780655704761.
• [3] S. Ramazan, R. Dimitrakopoulos, Production scheduling with uncertain supply: a new solution to the open pit mining problem. Optim. Eng. 14 (2), 361-380 (2013). DOI: https://doi.org/10.1007/s11081-012-9186-2.
• [4] J. Benndorf, R. Dimitrakopoulos, Stochastic long-term production scheduling of iron ore deposits: Integrating joint multi-element geological uncertainty. J. Min. Sci. 49 (1), 68-81 (2013). DOI: https://doi.org/10.1134/S1062739149010097.
• [5] A. Leite, R. Dimitrakopoulos, Stochastic optimisation model for open pit mine planning: Application and risk analysis at copper deposit. Min. Technol. 116 (3), 109-118 (2007). DOI: https://doi.org/10.1179/174328607X228848.
• [6] R.C. Goodfellow, R. Dimitrakopoulos, Global optimization of open pit mining complexes with uncertainty. Appl. Soft Comput. 40, 292-304 (2016). DOI: https://doi.org/10.1016/j.asoc.2015.11.038.
• [7] W.S. Liu, F.P. Li, Iron Ore Blending Based on Linear Programming. Applied Mechanics and Materials 443 (443-443), 657-661 (2013). DOI: https://doi.org/10.4028/www.scientific.net/AMM.443.657.
• [8] W.B. Lambert, A.M. Newman, Tailored Lagrangian Relaxation for the open pit block sequencing problem. Ann. Oper. Res. 222 (1), 419-438 (2014). DOI: https://doi.org/10.1007/s10479-012-1287-y.
• [9] N. Boland, I. Dumitrescu, G. Froyland, A.M. Gleixner, LP-based disaggregation approaches to solving the open pit mining production scheduling problem with block processing selectivity. Comput. Oper. Res. 36 (4), 1064-1089 (2007). DOI: https://doi.org/10.1016/j.cor.2007.12.006.
• [10] E. Moreno, M. Rezakhah, A. Newman, F. Ferreira, Linear models for stockpiling in open-pit mine production scheduling problems. European Journal of Operational Research. 260 (1), 212-221 (2016). DOI: https://doi.org/10.1016/j.ejor.2016.12.014.
• [11] R. Goodfellow, R. Dimitrakopoulos, Algorithmic integration of geological uncertainty in pushback designs for complex multiprocess open pit mines. Min. Technol. 122 (2), 67-77 (2013). DOI: https://doi.org/10.1179/147490013X13639459465736.
• [12] J.A. Ferland, J. Amaya, M.S. Djuimo, Application of a Particle Swarm Algorithm to the Capacitated Open Pit Mining Problem, Springer Berlin Heidelberg (2007).
• [13] M.S. Shishvan, J. Sattarvand, Long term production planning of open pit mines by ant colony optimization. Eur. J. Oper. Res. 240 (3), 825-836 (2015). DOI: https://doi.org/10.1016/j.ejor.2014.07.040.
• [14] M. Kumral, Optimizing ore-waste discrimination and block sequencing through simulated annealing. Appl. Soft Comput. 13 (8), 3737-3744 (2013). DOI: https://doi.org/10.1016/j.asoc.2013.03.005.
• [15] A. Khan, C. Niemann-Delius, Production Scheduling of Open Pit Mines Using Particle Swarm Optimization Algorithm. Adv. Oper. Res. 2014, 1-9 (2014). DOI: https://doi.org/10.1155/2014/208502.
• [16] L. Montiel, R. Dimitrakopoulos, Stochastic mine production scheduling with multiple processes: Application at Escondida Norte. Chile. J. Min. Sci. 49 (4), 583-597 (2013). DOI: https://doi.org/10.1134/S1062739149040096.
• [17] E. Jélvez, N. Morales, P. Nancel-Penard, J. Peypouquet, P. Reyes, Aggregation heuristic for the open-pit block scheduling problem. European Journal of Operational Research. 249 (3), 1169-1177 (2016). DOI: https://doi.org/10.1016/j.ejor.2015.10.044.
• [18] M. Soleymani Shishvan, J. Sattarvand, Long term production planning of open pit mines by ant colony optimization. European Journal of Operational Research. 240 (3), 825-836 (2015). DOI: https://doi.org/10.1016/j.ejor.2014.07.040.
• [19] S.O. Gilani, J. Sattarvand, Integrating geological uncertainty in long-term open pit mine production planning by ant colony optimization. Computers & Geosciences. 87, 31-40 (2016). DOI: https://doi.org/10.1016/j.cageo.2015.11.008.
• [20] D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. J. Glob. Optim. 39 (3), 459-471 (2007). DOI: https://doi.org/10.1007/s10898-007-9149-x.
• [21] D. Karaboga, B. Akay, A survey: Algorithms simulating bee swarm intelligence. Artif. Intell. Rev. 31 (1-4), 61-85 (2009). DOI: https://doi.org/10.1007/s10462-009-9127-4.
• [22] B. Li, Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm. Comput. Intel. Neurosci. 2014, 1-10 (2014). DOI: https://doi.org/10.1155/2014/270658.
• [23] B. Jafrasteh, N. Fathianpour, A hybrid simultaneous perturbation artificial bee colony and back-propagation algorithm for training a local linear radial basis neural network on ore grade estimation. Neurocomputing. 235, 217-227 (2017). DOI: https://doi.org/10.1016/j.neucom.2017.01.016.
• [24] C. Chen, Y. Tsai, R. Jhang, Approximation of the Piecewise Function Using Neural Fuzzy Networks with an Improved Artificial Bee Colony Algorithm. J. Autom. Control Eng. 3 (6), 503-506 (2015). DOI: http://dx.doi.org/10.12720/joace.3.6.503-506.
• [25] S. Anuar, A. Selamat, R. Sallehuddin, Hybrid Artificial Neural Network with Artificial Bee Colony Algorithm for Crime Classification. In: Phon-Amnuaisuk, S., Au, T. (eds) Computational Intelligence in Information Systems. Advances in Intelligent Systems and Computing. 331, 31-40 (2015). DOI: https://doi.org/10.1007/978-3-319-13153-5_4.
• [26] W.A.H.M. Ghanem, A. Jantan, Swarm intelligence and neural network for data classification. 2014 IEEE International Conference on Control System. Computing and Engineering (ICCSCE 2014). 196-201 (2014). DOI: https://doi.org/10.1109/ICCSCE.2014.7072714.
• [27] L. Cui, K. Zhang, G. Li, X. Wang, S. Yang, Z. Ming, J.Z. Huang, N. Lu, A smart artificial bee colony algorithm with distance-fitness-based neighbor search and its application. Future Gener. Comput. Syst. 89, 478-493 (2018). DOI: https://doi.org/10.1016/j.future.2018.06.054.
• [28] A. Tosun, Determination of Optimum Truck Number at Open Coal Quarry. Archives of Mining Sciences 66 (2), 213-225 (2021). DOI: https://doi.org/10.24425/ams.2021.137458.
• [29] F.M. Gema, M.L.R. Julio, P.G. Raul, M.P. Beatriz, P.A. Ruben, Multicriteria Analysis for the Determination of the Exploitability Index of Industrial Aggregate Outcrops. Archives of Mining Sciences 65 (1), 103-115 (2020). DOI:https://doi.org/10.24425/ams.2020.132709.
• [30] E. Van Tonder, D.A. Deglon, T.J. Napier-Munn, The effect of ore blends on the mineral processing of platinum ores. Min. Eng. 23 (8), 621-626 (2010). DOI: https://doi.org/10.1016/j.mineng.2010.02.008.
• [31] X.G. Su, X.Y. Chen, C. Liu, F. Yu, C.Y. Xu, P Index Model of Comprehensive Evaluation of Iron Ore Washability. Modern Mining 33 (03), 137-140+144 (2017).

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)