Bibliographic citations
Valenzuela, J., Bustamante, J., Cano, G., Zapana, O. (2023). Mejora de la precisión y predicción del Factor de Reconciliación F2 mediante el análisis de datos de Muestreo y Logueo para una Mina de Cobre a Tajo Abierto año 2021 - 2022 [Trabajo de investigación, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/675531
Valenzuela, J., Bustamante, J., Cano, G., Zapana, O. Mejora de la precisión y predicción del Factor de Reconciliación F2 mediante el análisis de datos de Muestreo y Logueo para una Mina de Cobre a Tajo Abierto año 2021 - 2022 [Trabajo de investigación]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2023. http://hdl.handle.net/10757/675531
@mastersthesis{renati/508034,
title = "Mejora de la precisión y predicción del Factor de Reconciliación F2 mediante el análisis de datos de Muestreo y Logueo para una Mina de Cobre a Tajo Abierto año 2021 - 2022",
author = "Zapana Farfan, Oscar Benedicto",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2023"
}
The motivation of this research work is given as a result of the implementation of mining reconciliation, which is a tool that is used for monitoring the management of mining processes, for the purposes of the study it addresses the F2 factor, which is transversal to the entire value chain, in principle the results of the year 2021 were monitored, in which the monthly reconciliation values were observed, obtaining 66.7% satisfactory reconciliation within the internationally acceptable range (+-5%), which indicates that due to excess or defect the production plans present the same precision, the analysis of the information with which the geological model is estimated was addressed, which is compared with the production of the milling and the re-handling of ore in the stocks, in the which opportunities for improvement were found through the systematization of field data collection through the development of an application for this purpose, with this development it was possible to improve correlation of field data which presented a correlation of 82%, the project was launched at the beginning of the year 2022 at the end of this period the correlation of the field information was improved obtaining 97% of correlation and consequently the reconciliation, obtaining a considerable improvement in the monthly reconciliation because 91.6% of the values were found within the desired range , which translates as a considerable improvement in the precision of production plans.
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