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Cutoff grades optimization in open pit mines using meta-heuristic algorithms

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  • Ahmadi, Mohammad Reza
  • Bazzazi, Abbas Aghajani

Abstract

To have a sound production planning one of the main factors that should be considered is the cutoff grade. The cutoff grade is used as a criterion to identify waste of minerals in a mining reserve. The cutoff grade is one of the most sensitive parameters that can have a significant impact on net present value (NPV) and cash flow of projects. Since the cutoff grade has a significant impact on the operation, the choice of the correct level of this grade is of considerable importance. Choosing the optimal cutoff grade maximizes the NPV and the total profit of the mining operation and the project. The optimization of the cutoff grades considering the maximum achievable NPV over the life of the mine is one of the key issues in the mining of open pits. In this paper, two different meta-heuristic optimization algorithms are employed to determine the optimal cutoff grade. For this purpose, taking into account the precision of 0.001%, the optimum cutoff grades, the production amount of each unit and the NPV are calculated. Accordingly, the optimum cutoff grades of iron mine No. 1 Golgohar was obtained using the PSO algorithm is 49.11–40.6%, and using the imperialist competitive algorithm, the optimum cutoff grades of iron mine No. 1 Golgohar was obtained from 48.56% to 40.5%. The results show that the determination of the cutoff grade by using these two methods has high accuracy and speed. According to the results, the ICA algorithm has a higher accuracy than the PSO algorithm.

Suggested Citation

  • Ahmadi, Mohammad Reza & Bazzazi, Abbas Aghajani, 2019. "Cutoff grades optimization in open pit mines using meta-heuristic algorithms," Resources Policy, Elsevier, vol. 60(C), pages 72-82.
    • Handle: RePEc:eee:jrpoli:v:60:y:2019:i:c:p:72-82
    DOI: 10.1016/j.resourpol.2018.12.001
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    References listed on IDEAS

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    5. Ahmadi, Mohammad Reza & Shahabi, Reza Shakoor, 2018. "Cutoff grade optimization in open pit mines using genetic algorithm," Resources Policy, Elsevier, vol. 55(C), pages 184-191.
    6. Mohammadi, Sadjad & Kakaie, Reza & Ataei, Mohammad & Pourzamani, Eshagh, 2017. "Determination of the optimum cut-off grades and production scheduling in multi-product open pit mines using imperialist competitive algorithm (ICA)," Resources Policy, Elsevier, vol. 51(C), pages 39-48.
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    1. Paithankar, Amol & Chatterjee, Snehamoy & Goodfellow, Ryan & Asad, Mohammad Waqar Ali, 2020. "Simultaneous stochastic optimization of production sequence and dynamic cut-off grades in an open pit mining operation," Resources Policy, Elsevier, vol. 66(C).
    2. Paithankar, Amol & Chatterjee, Snehamoy & Goodfellow, Ryan, 2021. "Open-pit mining complex optimization under uncertainty with integrated cut-off grade based destination policies," Resources Policy, Elsevier, vol. 70(C).
    3. Noriega, Roberto & Pourrahimian, Yashar, 2022. "A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning," Resources Policy, Elsevier, vol. 77(C).
    4. Biswas, Pritam & Sinha, Rabindra Kumar & Sen, Phalguni & Rajpurohit, Sohan Singh, 2020. "Determination of optimum cut-off grade of an open-pit metalliferous deposit under various limiting conditions using a linearly advancing algorithm derived from dynamic programming," Resources Policy, Elsevier, vol. 66(C).
    5. Biswas, Pritam & Sinha, Rabindra Kumar & Sen, Phalguni, 2023. "A review of state-of-the-art techniques for the determination of the optimum cut-off grade of a metalliferous deposit with a bibliometric mapping in a surface mine planning context," Resources Policy, Elsevier, vol. 83(C).
    6. Khan, Asif & Asad, Mohammad Waqar Ali, 2021. "A mixed integer programming based cut-off grade model for open-pit mining of complex poly-metallic resources," Resources Policy, Elsevier, vol. 72(C).
    7. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Nguyen-Thoi, Trung & Bui, Thu-Thuy & Nguyen, Nga & Vu, Diep-Anh & Mahesh, Vinyas & Moayedi, Hossein, 2020. "Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm," Resources Policy, Elsevier, vol. 66(C).

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