# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
## 1 Introduction  
Mine planning generally hinges on the accurate estimation of all anticipated costs and a detailed economic evaluation of the project [1]. When performed properly, the endeavor enables the forecast of capital and operating expenditures (CAPEX and OPEX) for informed decision-making as to the viability of the mining project. For a mining project, whether greenfield or brownfield, CAPEX estimates typically include equipment acquisition, pre-production stripping, working capital, and other fixed costs. In contrast, OPEX estimates include production-related costs associated with drilling, blasting, loading, hauling, administration, and beneficiation on a per-ton basis [2]. For mining enterprises, operating costs or OPEX are generally divided into three key components: mining, processing (sometimes referred to as milling), as well as general and administrative (G&A) expenses. The first two components represent the primary technical costs of a mining project, which cover the operation and maintenance of variousequipment, fuel and electricity consumption, and technical staff. G&A expenses (i.e., the third component) relate to costs associated with personnel management, legal and accounting services, and logistics, as well as other non-technical costs [3]. The estimation and modeling of costs associated with a mining project is a complex exercise; however, various approaches are available for the purpose. These include empirical models, activity-based costing, statistical methods, software-based estimation, and machine learning techniques [4; 5; 6; 7; 8; 9]. Mining projects are capital-intensive enterprises that entail multiple stages, namely exploration, development, production, closure, and rehabilitation. These stages generally span over many years, whether the project is developed as an open-pit or as an underground operation. Focusing on open-pit mines, each of the four stages mentioned above incurs significant operating costs depending on factors such as mine location, geology, equipment utilization, energy consumption, uncertainties, and risks [10; 11; 12; 13]. Understanding how operating costs are distributed along the value chain of the open pit mine is, therefore, essential for optimized mine planning. This is because this knowledge shapes decisions regarding resource allocation, equipment procurement, and production scheduling. By analyzing the distribution of costs along the value chain, mine managers can identify inefficiencies and explore opportunities for cost reduction. Several researchers have provided operating cost estimates of mining and quarrying operations. For example, [PERSON] et al. [14] analyzed the cost structure of mining equipment in a limestone quarry considering deposit conditions. They proposed five configurations of quarrying equipment, which varied based on the location of preliminary crushing, the number of machines, the number of operators, and the length of transport routes. [PERSON] et al. [15] studied the energy consumption, production, and costs in a surface coal mining operation in West Virginia. They found that diesel accounted for 75% of the energy used in coal and overburden extraction, while explosives, despite contributing only 6.22% of energy, made up nearly 50% of the total cost. Explosives had the highest unitary energy costs, while diesel and electricity were the least expensive. [PERSON] et al. [16] used Artificial Neural Networks (ANNs) to minimize oversized fragments and reduce surface mining costs in the Baikunth, Hirmi, Sonadih, and Rawan limestone quarries of India. Their trained ANN model accurately predicted the boulder count, providing a valuable tool for field engineers to optimize blast designs. This approach minimized boulder formation and improved the efficiency of downstream operations, resulting in significant cost savings. Various studies have investigated the transportation costs commonly incurred in open-pit mining operations [17; 18; 19; 20; 21].