Big Data on Decision Making in Energetic Management of Copper Mining


  • Carolina Lagos Facultad de Administración y Economí­a, Universidad de Santiago de Chile Av. Libertador Bernardo O’Higgins 3363, Santiago, Chile
  • Raúl Carrasco 1. Departamento de Ingenierí­a Eléctrica, Universidad de Santiago de Chile Av. Ecuador 3519, Santiago, Chile 2. Departamento de Matemí¡ticas y Ciencias de la Computación, Universidad de Santiago de Chile Las Sophoras 173, Santiago, Chile
  • Guillermo Fuertes Departamento de Ingenierí­a Industrial, Universidad de Santiago de Chile Av. Ecuador 3769, Santiago, Chile
  • Sabastií¡n Gutiérrez 1. Facultad de Ciencias Económicas y Administrativas, Universidad Central de Chile Lord Cochrane 417, Santiago, Chile 2. Facultad de Ingenierí­a, Universidad Andres Bello Antonio Varas 840, Providencia, Santiago, Chile
  • Ismael Soto Departamento de Ingenierí­a Eléctrica, Universidad de Santiago de Chile Av. Ecuador 3519, Santiago, Chile
  • Manuel Vargas Facultad de Ingenieria, Universidad Andres Bello, Santiago, Chile


Copper mining, energetic efficiency, big data, process management.


It is proposed an analysis of the related variables with the energetic consumption in the process of concentrate of copper; specifically ball mills and SAG. The methodology considers the analysis of great volumes of data, which allows to identify the variables of interest (tonnage, temperature and power) to reach to an improvement plan in the energetic efficiency. The correct processing of the great volumen of data, previous imputation to the null data, not informed and out of range, coming from the milling process of copper, a decision support systems integrated, it allows to obtain clear and on line information for the decision making. As results it is establish that exist correlation between the energetic consumption of the Ball and SAG Mills, regarding the East, West temperature and winding. Nevertheless, it is not observed correlation between the energetic consumption of the Ball Mills and the SAG Mills, regarding to the tonnages of feed of SAG Mill. In consequence, From the experimental design, a similarity of behavior between two groups of different mills was determined in lines process . In addition, it was determined that there is a difference in energy consumption between the mills of the same group. This approach modifies the method presented in [1].


C. Lagos, F. Cordova, S. Gutierrez, G. Fuertes, and R. Carrasco, Data analysis methods related to energetic consumption in copper mining . A test case in Codelco, Computers Communications and Control (ICCCC), 2016 6th International Conference on, IEEE Xplore, e-ISSN 978-1-5090-1735-5, doi: 10.1109/ICCCC.2016.7496768, 1241-247, 2016.

J. A. Aloysius, H. Hoehle, S. Goodarzi, and V. Venkatesh (2016), Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes, Annals of Operations Research, 1-27.

G. Zucker, J. Malinao, U. Habib, T. Leber, A. Preisler, F. Judex (2014), Improving energy efficiency of buildings using data mining technologies, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), 2664-2669.

T. Mokfi, M. Almaeenejad, and M. M. Sedighi, A Data Mining Based Algorithm to Enhance Maintenance Management: A Medical Equipment Case Study, 2011 First International Conference on Informatics and Computational Intelligence, 74-80, IEEE, dec 2011.

P. Bastos, I. d. S. Lopes, and L. Pires (2012), A maintenance prediction system using data mining techniques, World Congress on Engineering 2012, 1448-1453.

F. Filip (2008); Decision support and control for large-scale complex systems, Annual Reviews in Control, 32(1): 61-70, 2008.

A. Kaklauskas (2015), Biometric and Intelligent Decision Making Support, vol. 81, Springer International Publishing AG, 2015.

M. M. Polycarpou and Y. Ohta (2007), Nonlinear Fault Diagnosis of Dynamical Systems: An Intelligent Control Framework, IFAC Proceedings Volumes, 40(9):1-1.

L. Martín, L. Baena, L. Garach, G. López, and J. de O-a (2014), Using data mining techniques to road safety improvement in Spanish roads, Procedia-Social and Behavioral Sciences, 160:607-614.

M. A. Khan, M. Z. Islam, and M. Hafeez (2012), Evaluating the performance of several data mining methods for predicting irrigation water requirement, Proceedings of the Tenth Australasian Data Mining Conference, Australian Computer Society, 134:199-207.

C. Gröger, F. Niedermann, and B. Mitschang (2012), Data mining-driven manufacturing process optimization,Proceedings of the World Congress on Engineering, 3:4-6.

N. Altintas and M. Trick (2014), A data mining approach to forecast behavior, Annals of Operations Research, 216:3-22.

R. Carrasco, M. Vargas, M. Alfaro, I. Soto, and G. Fuertes (2015); Copper Metal Price Using Chaotic Time Series Forecating, IEEE Latin America Transactions, 13(6):1961-1965,

F. Seguel, R. Carrasco, M. Alfaro, P. Adasme, and I. Soto, A Meta-heuristic Approach for Copper Price Forecasting, Information and Knowledge Management in Complex Systems, 449: 156-165, 2015.

C. Lagos, G. Fuertes, R. Carrasco, S. Gutierrez, M. Vargas, and R. Rodrigues, Facing the data analysis complexity for the energetic efficiency management at great copper, IEEE ICA/ACCA 2016, (Curicó), 1-6, 2016.

U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth (1996), From Data Mining to Knowledge Discovery in Databases, AI Magazine, v17(3):37-46.

J. Ding, Q. Chen, T. Chai, H. Wang, and C.-Y. Su (2009), Data mining based feedback regulation in operation of hematite ore mineral processing plant, 2009 American Control Conference, IEEE, 907-912.

D. Rojas, E. Castillo, and J. Cantallopts (2015), Caracterización de los costos de la gran minería del cobre, Tech. Rep., Comisión Chilena del Cobre, 2015.

D. Hodouin, S.-L. Jämsä-Jounela, M. Carvalho, and L. Bergh (2001), State of the art and challenges in mineral processing control, Control Engineering Practice, 9(9):995-1005.

Codelco, Memoria Anual 2015, tech. rep., Codelco, Santiago of Chile, 2015.

Y. Y. Wen, W. M. Huang, J. Wu, Y. Chen, J. Q. Song (2013), Water consumption analysis system based on data mining, Applied Mechanics and Materials, 241:1093-1097.

Y. Liu, J. Zhao, and Z. Wang (2015), Identifying determinants of urban water use using data mining approach, Urban Water Journal, 12(8):618-630.

R. C. Leme et al. (2014); Design of experiments applied to environmental variables analysis in electricity utilities efficiency: The Brazilian case, Energy Economics, 45:111-119.

Y. Liu and C. Li (2016), Complex-valued Bayesian parameter estimation via Markov chain Monte Carlo, Information Sciences, 326:334-349.

X. Xing, K. Wang, T. Yan, Z. Lu (2016), Complete canonical correlation analysis with application to multi-view gait recognition, Pattern Recognition, 50: 107-117.

Z. Chen, K. Zhang, S. X. Ding, Y. A. Shardt, and Z. Hu (2016), Improved canonical correlation analysis-based fault detection methods for industrial processes, Journal of Process Control, 41:26-34.

Z. Chen, S. X. Ding, K. Zhang, Z. Li, and Z. Hu (2016), Canonical correlation analysisbased fault detection methods with application to alumina evaporation process, Control Engineering Practice, 46:51-58.

L. Shang, J. Liu, K. Turksoy, Q. Min Shao, A. Cinar (2015), Stable recursive canonical variate state space modeling for time-varying processes, Control Engineering Practice, 36: 113-119.

D. C. Montgomery (2013); Design and Analysis of Experiments Eighth Edition, John Wiley & Sons, Inc., 8th ed., 2013.

J. Tang, G. Gong, H. Su, F. Wu, and C. Herman (2016); Performance evaluation of a novel method of frost prevention and retardation for air source heat pumps using the orthogonal experiment design method, Applied Energy, 169:696-708.

G. S. dos Reis et al., (2016); The use of design of experiments for the evaluation of the production of surface rich activated carbon from sewage sludge via microwave and conventional pyrolysis, Applied Thermal Engineering, 93: 590-597.



Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.