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 Matemáticas 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
  • Sabastián 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


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].


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How to Cite
LAGOS, Carolina et al. Big Data on Decision Making in Energetic Management of Copper Mining. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 12, n. 1, p. 61-75, dec. 2016. ISSN 1841-9844. Available at: <>. Date accessed: 07 july 2020. doi:


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