Multi-Objectives Optimization of Grade Index based on IPSO
Authors
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College of environment and resources, Fuzhou University, Fuzhou, Fujian, 350 116 ,CN
Ping Huang -
Guizhou Xinlian Blasting Engineering Group Co., Ltd., Guiyang, Guizhou, 550 002 ,CNTiejun Tao
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Postdoctorate Station of Mining Industry Engineering, Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, Yunnan, 650 093 ,CNJie Liu
Keywords:
Constraints, Grade Index, Improved Particle Swarm Optimization, Multi-Objectives Optimization.Abstract
According to the specific characteristic of multi-mental mine, the function models of metal production, gross profit, net present value, rate of return on investment were obtained. Then the multi-objective optimization of ore grade index was achieved under constraints. The model was calculation based on the method of improved particle swarm optimization (IPSO). In the 5 years’ evaluation, the new index scheme can increase total profit about 45.25 million yuan, total amount of metal mines about 23,214 tons, net present value about 26.29 million yuan and the rate of return on investment about 11.25 %.
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References
Dowd, P. A. (1976): Application of dynamic and stochastic programming to optimize cutoff grades and production rates. Transactions of the Institute of Mining and metallurgy, 85(3): 16-21.
Talor, H. K. (1976): General Background Theory of Cut-off Grade. Transaction (section A), institute of Mining and metallurgy, 81(3): 12-16.
Lane, K. F. (1976): Choosing the optimum cutoff grade. Transactions of Colorado Mining School. 59(4): 35-40.
Oberwinkler (2004): From real time to production optimization. (2004). Proceeding of the SPE Asia Pacific Conference on integrated Modeling for Asset Management, (3): 91-104.
Minnitt, R. C. A. (2004): “Cut-off grade determination for the maximum value of a small wits-type gold mining operation.” Journal of the South African Institute of Mining and Metallurgy, 104(5): 277-283.
Abrishamifar., S. A. (2004): “Open pit optimization including mineral dressing criteria using 0~1 non-liner goal programming.” Mining of Technology, 113(1):3~16.
Annicchiarico, W. (2016): “Study of the behaviour of structures made with expanded metal under axial compression.” Revista de la Facultad de Ingeniería, 31(1): 1-15.
Martínez, G., Graciano, C., Casanova, E. and Pelliccionil, O. (2016): “Behaviour of estructural meshes of expanded metal under traction loads.” Revista de la Facultad de Ingeniería, 31(1): 268-292.
Qing, W. (2003): Long-term open-pit production scheduling through dynamic phase-bench sequencing. Transction of the Institute of Mining and Metallurgy, 105(8): 99~144.
Wilke, F. L., Mueller, K. and Wright, E. (1984): Ultimate Pit and Production Scheduling Optimization. Proceeding of the 18th APCOM Symposium.
Zhang, Y. D., Cai, Q. X., Wu, L. X. and Zhang, D. X. (1992): Combined Approach for Surface Mine Short Term Planning Optimization. Proceeding of the 23th APCOM Symposium.
Zimmermann, H. J. (1978): “Fuzzy programming linear Programming with several objective functions.” Fuzzy Sets and Systems, 1978, l: 45-55.
Jang, J. S. R. (1993): “ANFIS: Adaptive-Network-based Fuzz-y Inference Systems.” IEEE Transactions on Systems, Man, and Cybernetics, 2 3(3):665~685.
Jang, J. S. R. and Sun, C. T. (1995): Neuro-fuzzy Modeling and Control. Proceedings of the IEEE, 83(3): 378-406.
Lee, Hahn-Ming, Lu, Bing-hui, Lin, Fu-Tyan and Tang, Chun- (1995): “A fuzzy neural network model for revising imperfect fuzzy rules.” Fuzzy Sets and Systems. 76: 25-45.
Chao, C. T. and Teng, C. C. (1995): “Implementation of a fuzzy inference system using a normalized fuzzy neural network.” Fuzzy Sets and Systems. 75: 17-31.
