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<p> Open-pit mine planning plays a crucial role in defining mineral reserves and optimizing their extraction. The extraction process typically involves mining sequential pits known as pushbacks, which must satisfy complex geometrical constraints to ensure operational efficiency and safe equipment usage. However, current pushback designs are largely manual, relying heavily on the expertise of engineers to translate outputs from commercial tools into practical solutions. This article introduces a novel algorithm inspired by the physical principles of soap bubbles, leveraging their natural tendency to form compact, efficient shapes. The algorithm integrates a new mathematical formulation that simultaneously considers both the economic value and geometric characteristics of pushbacks, producing compact and operationally feasible clusters of blocks. Extensive testing on real mines with large datasets demonstrates the algorithm’s capability to generate practical pushbacks that meet both economic and operational requirements.</p>

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Developing and Testing a Soap Bubble-Based Model for Practical Open-Pit Pushback Design

  • 期刊论文

  • 发表日期：

    2025年3月7日

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  • Juan L. Yarmuch,
  • Hyam Rubinstein

摘要

Open-pit mine planning plays a crucial role in defining mineral reserves and optimizing their extraction. The extraction process typically involves mining sequential pits known as pushbacks, which must satisfy complex geometrical constraints to ensure operational efficiency and safe equipment usage. However, current pushback designs are largely manual, relying heavily on the expertise of engineers to translate outputs from commercial tools into practical solutions. This article introduces a novel algorithm inspired by the physical principles of soap bubbles, leveraging their natural tendency to form compact, efficient shapes. The algorithm integrates a new mathematical formulation that simultaneously considers both the economic value and geometric characteristics of pushbacks, producing compact and operationally feasible clusters of blocks. Extensive testing on real mines with large datasets demonstrates the algorithm’s capability to generate practical pushbacks that meet both economic and operational requirements.