Scholarlink

<p>Mine planning traditionally involves three critical subproblems that are typically solved sequentially: capacity planning (selecting maximum possible production rates), cut-off grade selection (deciding material destinations), and block sequencing (determining extraction order). However, these decisions are fundamentally interdependent, creating circular reference loops where each decision influences the others. This interdependency means that combining individually optimal solutions cannot guarantee overall optimization, yet current approaches fail to address these relationships adequately. This study presents a comprehensive workflow that simultaneously optimizes these three interdependent subproblems using mixed integer programming (MIP) combined with grid search methodology. The approach leverages cost-capacity relationships derived from historical data of similar mining projects to develop empirical models representing economies of scale (EoS) effects on mining costs, processing costs, capital recovery, and metal recovery parameters. These empirical models replace subjective theoretical assumptions about EoS with data-driven relationships. A systematic three-phase grid search method efficiently explores the solution space to determine optimal capacity configurations, while MIP optimizes block sequencing and material destinations for each capacity scenario. The model’s effectiveness is demonstrated through a case study of a gold deposit, where it successfully identified an optimal configuration of 20.79 million tonnes per year mining capacity and 15 million tonnes per year processing capacity. Results reveal that processing capacity has a more significant impact on project value than mining capacity, highlighting the critical importance of balanced capacity planning for both mining and mineral processing operations. This integrated optimization approach provides mining operators with a more robust decision-making framework that properly accounts for the complex interdependencies of operational parameters, leading to improved project economics compared to traditional sequential optimization methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Mine Planning Optimization Under Circular Reference Loop Characteristics: A Workflow Combining Grid Search and Mixed Integer Programming

  • 期刊论文

  • 发表日期：

    2025年9月3日

查看全文
  • Mehmet Balci,
  • Sena Senses,
  • Mustafa Kumral

摘要

Mine planning traditionally involves three critical subproblems that are typically solved sequentially: capacity planning (selecting maximum possible production rates), cut-off grade selection (deciding material destinations), and block sequencing (determining extraction order). However, these decisions are fundamentally interdependent, creating circular reference loops where each decision influences the others. This interdependency means that combining individually optimal solutions cannot guarantee overall optimization, yet current approaches fail to address these relationships adequately. This study presents a comprehensive workflow that simultaneously optimizes these three interdependent subproblems using mixed integer programming (MIP) combined with grid search methodology. The approach leverages cost-capacity relationships derived from historical data of similar mining projects to develop empirical models representing economies of scale (EoS) effects on mining costs, processing costs, capital recovery, and metal recovery parameters. These empirical models replace subjective theoretical assumptions about EoS with data-driven relationships. A systematic three-phase grid search method efficiently explores the solution space to determine optimal capacity configurations, while MIP optimizes block sequencing and material destinations for each capacity scenario. The model’s effectiveness is demonstrated through a case study of a gold deposit, where it successfully identified an optimal configuration of 20.79 million tonnes per year mining capacity and 15 million tonnes per year processing capacity. Results reveal that processing capacity has a more significant impact on project value than mining capacity, highlighting the critical importance of balanced capacity planning for both mining and mineral processing operations. This integrated optimization approach provides mining operators with a more robust decision-making framework that properly accounts for the complex interdependencies of operational parameters, leading to improved project economics compared to traditional sequential optimization methods.