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Calculating ultimate pit limits and determining pushbacks in open-pit mining projects

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  • Buelga Díaz, Arturo
  • Diego Álvarez, Isidro
  • Castañón Fernández, César
  • Krzemień, Alicja
  • Iglesias Rodríguez, Francisco Javier

Abstract

A very important part of any open-pit mining project is determining the ultimate pit limits and optimizing the pushbacks. Currently, block models are the most widely used for calculating mineral resources and reserves. Block models can define the ultimate pit limits through algorithms like floating cone, Lerchs-Grossmann, PseudoFlow, etc. However, only through an economic study will the best pits be selected, from most to least economically valuable, and the different possible pushbacks can then be defined.

Suggested Citation

  • Buelga Díaz, Arturo & Diego Álvarez, Isidro & Castañón Fernández, César & Krzemień, Alicja & Iglesias Rodríguez, Francisco Javier, 2021. "Calculating ultimate pit limits and determining pushbacks in open-pit mining projects," Resources Policy, Elsevier, vol. 72(C).
    • Handle: RePEc:eee:jrpoli:v:72:y:2021:i:c:s0301420721000751
    DOI: 10.1016/j.resourpol.2021.102058
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    References listed on IDEAS

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    1. Sánchez Lasheras, Fernando & de Cos Juez, Francisco Javier & Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro, 2015. "Forecasting the COMEX copper spot price by means of neural networks and ARIMA models," Resources Policy, Elsevier, vol. 45(C), pages 37-43.
    2. Jaunky, Vishal Chandr, 2013. "A cointegration and causality analysis of copper consumption and economic growth in rich countries," Resources Policy, Elsevier, vol. 38(4), pages 628-639.
    3. Khan, Asif & Asad, Mohammad Waqar Ali, 2019. "A method for optimal cut-off grade policy in open pit mining operations under uncertain supply," Resources Policy, Elsevier, vol. 60(C), pages 178-184.
    4. Matyjaszek, Marta & Riesgo Fernández, Pedro & Krzemień, Alicja & Wodarski, Krzysztof & Fidalgo Valverde, Gregorio, 2019. "Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory," Resources Policy, Elsevier, vol. 61(C), pages 283-292.
    5. Sterba, Jiri & Krzemień, Alicja & Fidalgo Valverde, Gregorio & Diego Álvarez, Isidro & Castañón Fernández, César, 2020. "Energy-sustainable industrialized growth in the Czech Republic: The Cínovec lithium mining project," Resources Policy, Elsevier, vol. 68(C).
    6. Riesgo García, María Victoria & Krzemień, Alicja & Sáiz Bárcena, Lourdes Cecilia & Diego Álvarez, Isidro & Castañón Fernández, César, 2019. "Scoping studies of rare earth mining investments: Deciding on further project developments," Resources Policy, Elsevier, vol. 64(C).
    7. Riesgo García, María Victoria & Krzemień, Alicja & Manzanedo del Campo, Miguel Ángel & Escanciano García-Miranda, Carmen & Sánchez Lasheras, Fernando, 2018. "Rare earth elements price forecasting by means of transgenic time series developed with ARIMA models," Resources Policy, Elsevier, vol. 59(C), pages 95-102.
    8. Krzemień, Alicja & Riesgo Fernández, Pedro & Suárez Sánchez, Ana & Diego Álvarez, Isidro, 2016. "Beyond the pan-european standard for reporting of exploration results, mineral resources and reserves," Resources Policy, Elsevier, vol. 49(C), pages 81-91.
    9. Jean-Claude Picard, 1976. "Maximal Closure of a Graph and Applications to Combinatorial Problems," Management Science, INFORMS, vol. 22(11), pages 1268-1272, July.
    10. Marta Matyjaszek & Gregorio Fidalgo Valverde & Alicja Krzemień & Krzysztof Wodarski & Pedro Riesgo Fernández, 2020. "Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production," Energies, MDPI, vol. 13(8), pages 1-15, April.
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    1. Madziwa, Lawrence & Pillalamarry, Mallikarjun & Chatterjee, Snehamoy, 2023. "Integrating stochastic mine planning model with ARDL commodity price forecasting," Resources Policy, Elsevier, vol. 85(PB).

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