Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine

Abstract

This study analyzes the distribution of operating costs along the value chain of an open-pit copper mine with a focus on key operational units or operations such as drilling, blasting, loading, hauling, stockpiling, blending, crushing, milling, and flotation. Using process costing analysis, key cost drivers were identified, and their individual contributions to total expenses were quantified. Results revealed that comminution processes dominate the operational cost structure, with milling accounting for 6.18 USD/ton, representing 59.1% of total operating costs, and crushing costing 1.15 USD/ton, that is, 11% of total operating expenditure. The study also highlighted several opportunities for cost reduction and enhanced mining sustainability through strategies such as energy consumption optimization, the use of alternative energy sources, and optimized blast design. Finally, valuable insights aimed at promoting sustainable resource utilization, improved cost efficiency, and data-driven decision-making in mining operations are offered to mine planners and operators. This is eventually expected to lay the foundation for benchmarking work on the establishment of a baseline and standards for similar mining operations.

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 various equipment, 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, Patyk 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. Kecojevic 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. Dhekne 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]. These studies aimed to optimize transport systems to improve efficiency and minimize operating costs. Furthermore, Ozdemir and Kumar [22] developed a cost model to minimize total mining costs for bench production in open-pit coal mines. Given their impact on all mining activities, they incorporated fragmentation size and blast-hole diameter as key decision variables and used an evolutionary algorithm to optimize operation costs. Shafiee et al. [23] developed an econometric model for estimating total operating cost based on data from 20 existing open-pit coal mines in Australia. The model focused on four independent variables, including deposit average thickness, stripping ratio, capital cost, and daily production rate, with operating cost as the dependent variable. The results indicated that capital cost and production rate have a negative effect on operating cost, while deposit average thickness and stripping ratio have a positive effect. In their study, Segura-Salazar et al. [24] performed a pre-feasibility assessment of four processing routes for an open-pit iron mine, employing different technologies with an emphasis on the stages of comminution and classification. Considering CAPEX and OPEX estimates as economic indicators, routes based on High-Pressure Grinding Rolls (HPGRs) and Vertical Roller Mill (VRM) technologies were found to be particularly attractive for the beneficiation of the itabirite iron ore studied. In addition, OPEX estimates were limited to liners, grinding media, and energy consumption. Navarro Torres et al. [25] conducted an economic analysis of a mine-to-crusher model to estimate and minimize operating expenses for a truckless open-pit iron mine. The calibration of this model was based on rock fragmentation from the blasting phase. By considering the structural lithologies of canga and jaspillites, the authors concluded that the 80% passing size (P80) value that minimizes overall operating costs ranges from 0.31 to 0.34 m. Marinin et al. [26] estimated the production costs of an open-pit gold mine with multiple deposits, considering factors such as dilution and losses. They showed that managing ore quality parameters through internal reserves use reduces the operating costs of the processed rock mass across the entire production chain.

In an interesting sequence of studies, mining operating costs in Chile, the world’s largest copper producer, have been analyzed and reported on. For example, Knights and Oyanader [27] conducted a maintenance benchmarking study across six open-pit copper mines, with mill capacities ranging from 18,000 t/d to 156,000 t/d. These mines collectively accounted for 58% of Chile’s copper production in 2000. The study found that, on average, maintenance costs represented 44% of total mine production costs, including operating and maintenance costs but excluding G&A expenses. Fernandez [28] found that declining ore grades in the porphyry copper deposits of northern Chile led to rising mining costs (including cash costs, depreciation, and indirect interest costs) from 1980 to 2015. From 2001 to 2015, concentrator plants saw an 87.1% increase in electricity costs, compared to a 48.1% rise in mining costs. He also presented an index of total unit costs for 2005–2015, including operating, financial, and non-operating expenses, revealing that Escondida had the largest cost increase among the ten largest private mining companies operating in Chile. González et al. [29] conducted a sensitivity analysis of operating costs for an ex-post net present value (NPV) assessment to evaluate the potential impact of efficiency gains from privatizing mining operations (as a hypothetical counterfactual scenario) on nationalized mining operations between 1967 and 2022. The analysis revealed that a reduction in operating costs exceeding 4.5% would lead to a negative NPV, making the counterfactual scenario more advantageous than the base case.

Most studies on the operating costs of open-pit mines focus on individual or combined units or operations, such as drilling, blasting, loading, hauling, or crushing. In addition to this, available research often highlights the impact of cost drivers (e.g., fuel consumption, maintenance requirements, and equipment utilization) on these isolated units [30,31,32,33,34,35,36,37]. As such, there seems to be little work on the distribution of operating costs across the entire mine value chain, from extraction to processing. This study attempts to address the gap around operating cost estimates and their distribution along the value chain of an open-pit copper mine. The costs associated with key operational units, including drilling, blasting, loading, hauling, stockpiling, blending, comminution, and flotation, are reviewed in terms of their magnitude and share. Furthermore, a comprehensive breakdown of operating costs is done between mining and processing operations, thereby delivering valuable insights to support strategic mine planning.

Lastly, it is important to state that this paper focuses on the internal operational costs spanning from blasting to flotation for a selected copper mine. Ancillary mineral processing stages, such as dewatering and tailings disposal, are beyond the scope of the intended study. Equally, operations pertaining to extractive metallurgy, along with external factors like transportation, environmental management, and reclamation, although critical to the holistic understanding of the cost structure of the mining value chain, are also not included in the study.

2. Materials and Methods

2.1. The Open-Pit Mine

This case study focuses on an open-pit mine in the southeast Democratic Republic of the Congo (DRC), producing copper (Cu) within the Central African Copperbelt. The deposit is hosted in the Neoproterozoic metasedimentary rocks of the Katangan Supergroup, which is closely associated with the external fold-and-thrust belt of the Lufilian Arc, a prominent arc-shaped structural zone. This belt extends approximately 2000 km in a northeast-southwest direction, traversing Meso-to-Neoproterozoic sedimentary basins across the continent from the Atlantic Ocean in the southwest. The Katangan Supergroup is stratigraphically divided into three distinct groups, listed from top to bottom: Kundelungu (Ku), Nguba (Ng), and Roan (R). The Roan Group is further subdivided into four subgroups, including the Mines (R2) and Dipeta (R3) subgroups, which host the primary Copper-Cobalt mineralization. The deposit itself is situated within a geologically complex setting that includes a variety of rock formations, such as dolomitic, graphitic, albitized shale, conglomerate, and quartzite. The mineralization is distributed across three distinct zones: the oxide zone, the mixed or transitional zone, and the sulfide zone. The oxide zone is characterized by copper oxide minerals like chrysocolla and malachite. The mixed zone, on the other hand, contains both oxide and sulfide mineralized ore bodies. In the case of the sulfide zone, it is dominated by copper sulfide minerals such as chalcopyrite, chalcocite, and bornite [38,39].

In terms of operational requirements, the mine employs a drill-and-blast technique alongside a truck-and-shovel system. Out of 350 scheduled operating days per year, 50 are dedicated to maintenance and repairs, resulting in 300 effective working days. Mining activities run on two 10 h shifts per day aimed at extracting a run-of-mine (ROM) of rock density 2.5 t/m³. The processing plant includes three units (i.e., crushing, milling, and flotation) designed to produce a concentrate grading 28% Cu. The plant has an average annual capacity of 10,350,000 metric tons of ore and is operated in two 12 h shifts per day over 345 effective days each year. With an availability rate of 94.5%, the plant processes ore with an average head grade of 2.5% Cu.

2.2. Research Methodology

The objective of this study is to profile the operating cost distribution across the mine value chain of an open-pit copper mine from drilling to flotation. To this end, costs were estimated across the entire mining-to-processing workflow by means of process costing. In other words, a cost-accounting method was used to calculate the average operating expenses (OPEX) incurred by each unit or operation to produce a metric ton of ore extracted or processed over a specific period.

The data used for this study were derived from historical operational records, financial reports, and production data of the mine over a period of five years (i.e., 2018–2022). Operating costs across the value chain were estimated as a five-year average of annual costs reported for each key unit.

In terms of the implementation of the process costing methodology, costs were gathered and allocated to specific units of the value chain following the four steps below:

• In the context of this study, the mine value chain was first segmented into mining and processing. Mining operations or units included drilling, blasting, loading, hauling, stockpiling, and blending. Processing operations, on the other hand, encompassed comminution and flotation. Each operation represented a distinct phase of the value chain, with costs being tracked individually for each unit. Figure 1 provides an overview of the different units across the value chain as defined in this study;
• Costs were then accumulated for each unit over one year. Cost accumulation included equipment depreciation, labor expenses, fuel and energy consumption, maintenance and repair costs, consumables such as explosives, flotation reagents, wear parts and other chemicals, and contingency funds (10%) for unexpected expenses. This step ensured that all relevant costs were comprehensively captured for each operational unit, providing a clear view of their individual contribution to the financial performance of the value chain;
• Once the costs were accumulated, the average operating cost per unit was calculated by dividing the total accumulated costs by the metric tons extracted or processed over a one-year period;
• In the final step, the average operating costs per unit in the entire mine value chain were summed up to determine the total operating cost. This provided insights into the overall cost structure for mining and processing a metric ton of material. By aggregating costs across all units, from extraction to final processing, the holistic assessment of total operating expenses could highlight areas for potential cost reduction.

Ultimately, the average operating costs per unit across the entire mine value chain were compared with published industry benchmarks to provide context for the research findings.

2.3. Data Presentation

The annual operating cost components along the mine value chain are presented in

Table 1. Operating cost components data along the mine value chain.

Operation	Components	Cost (USD/Year)	%
	Maintenance and Repairs	663,600	5
	Labor	227,700	2
Drilling	Fuel	5,702,400	41
	Equipment Depreciation	298,620	2
	Consumables	6,912,000	49
	Other	172,800	1
	Explosives and Accessories	21,600,000	97.9
	Labor	72,450	0.3
Blasting	Fuel	103,680	0.5
	Permits and Regulatory	200,000	0.9
	Other	86,400	0.4
	Maintenance and Repairs	2,112,000	9
	Labor	289,800	1
Loading	Fuel	14,400,000	59
	Equipment Depreciation	1,584,000	6
	Consumables	5,760,000	24
	Other	288,000	1
	Maintenance and Repairs	3,583,140	8.7
	Labor	993,600	2.4
Hauling	Fuel	25,920,000	63.2
	Equipment Depreciation	4,209,213.46	10.3
	Consumables	5,760,000	14
	Other	576,000	1.4
	Maintenance and Repairs	282,417.15	37
	Labor	140,760	18
	Fuel	100,500	13
Stockpiling	Storage	29,668.80	4
	Equipment Depreciation	113,213.09	15
	Inventory Management	79,000	10
	Other	19,351.77	3
	Maintenance and Repairs	71,500	20
	Labor	54,280	16
	Fuel	82,500	24
Blending	Equipment Depreciation	49,500	14
	Sampling and Testing	43,051.11	12
	Quality Control	32,288.33	9
	Other	16,655.98	5
	Maintenance and Repairs	990,000	9
	Labor	653,329.01	6
Crushing	Energy Consumption	6,024,942	56
	Equipment Depreciation	534,600	5
	Consumables	2,409,976.80	22
	Other	212,256.96	2
	Maintenance and Repairs	1,123,200	1.9
	Labor	846,654.77	1.5
Milling	Energy Consumption	34,620,750	59.5
	Equipment Depreciation	522,578.57	0.9
	Consumables	20,772,450	35.7
	Other	289,428.17	0.5
	Maintenance and Repairs	415,170	2
	Labor	554,663.85	2.6
Flotation	Energy Consumption	2,430,594	11.5
	Equipment Depreciation	483,634.17	2.3
	Consumables and Reagents	16,783,240	79.6
	Other	413,346.04	2

. These costs are broken down into the elementary units constituting the building blocks of the value chain spanning from drilling to flotation. These annual unit costs are provided along with their equivalent percent fractions of the total cost.

2.4. Estimation of Operating Costs

This section details the operating parameters derived from the historical and production data of the mine, along with the equations employed to estimate operating costs across the entire mine value chain, from drilling to flotation.

2.4.1. Drilling

Blast holes were drilled vertically using a rotary machine with a hole of diameter 127 mm and a depth of 10.5 m for a bench height of 10 m. The burden and spacing of the blast design ranged between 3.5–4 m and 3.5–4 m, respectively. A staggered drilling pattern was employed with a sub-drilling depth of 0.5 m. An average of 44,352,000 tons of material was drilled annually, corresponding to 53,222 drilled blast holes.

The operating cost associated with drilling was determined as follows:

$$K_{Dt}=K_{Dmr}+K_{Dlab}+K_{Df}+K_{Deqd}+K_{Dc}+K_{DO}$$

(1)

$$OPE{X}_D={\displaystyle \frac{K_{Dtc}}{M_D}}$$

(2)

where K_Dt is the total cost of drilling (USD/year); K_Dmr is the cost of maintenance and repairs (USD/year); K_Dlab is the labor cost inclusive of salaries and wages for drill operators and technicians (USD/year); K_Df is the cost of fuel associated with drilling (USD/year); K_Deqd is the cost of equipment depreciation (USD/year); K_Dc is the cost of drilling consumables (USD/year); K_DO is the other costs (USD/year); OPEX_D is the drilling operating cost (USD/ton); K_Dtc is the total cost inclusive of the 10% contingency (USD/year); and M_D is the annual volume of material drilled (tons/year).

2.4.2. Blasting

The blasting operation used Emulsion S100 explosives with a density of 1.2 g/cm³, a relative weight strength (RWS) of 85% (with ANFO = 100%), and a velocity of detonation (VOD) of 5000 m/s. The powder factor ranged from 0.4 to 1.2 kg/m³ for a rock mass of specific gravity of 2.5 g/cm³. Drill cuttings were used as stemming material with depths ranging between 3 and 4 m. An average of 55,440,000 tons of material was blasted annually, representing a 25% increase over the annual volume of material drilled due to swelling during blasting. This translated into 18,480 tons of explosives required per year.

The blasting operating cost was estimated using the following equations:

$$K_{Bt}=K_{Bms}+K_{Blab}+K_{Bf}+K_{Bprg}+K_{BO}$$

(3)

$$OPE{X}_B={\displaystyle \frac{K_{Btc}}{M_B}}$$

(4)

where K_Bt is the total cost of blasting (USD/year); K_Bms is the cost of the material and supplies, including explosives and blasting accessories (USD/year); K_Blab is the labor cost inclusive of salaries and wages for blast operators and technicians (USD/year); K_Bf is the cost of fuel associated with blasting (USD/year); K_Bprq is the regulatory cost associated with permits, blasting licenses, insurances, and disposal of blasting consumables (USD/year); K_BO is the other costs (USD/year); OPEX_B is the blasting operating cost (USD/ton); K_Btc is the total cost inclusive of the 10% contingency (USD/year); and M_B is the annual volume of material blasted (tons/year).

2.4.3. Loading

After blasting, the material was loaded using a fleet of twelve excavators, with bucket capacities ranging from 4.5 to 15 m³ and a fill factor of around 0.9. The excavators were operated at an average cycle time of 3 min and an operational availability of 90%, resulting in an average hourly loading capacity of 397 m³. As an annual target, the total material loaded was expected to reach an average of 52,800,000 tons. This represents approximately a 5% decrease compared to the annual volume of material blasted due to compaction during loading.

The operating cost of loading was estimated using the following equations:

$$K_{Lt}=K_{Lmr}+K_{Llab}+K_{Lf}+K_{Leqd}+K_{Lc}+K_{LO}$$

(5)

$$OPE{X}_L={\displaystyle \frac{K_{Ltc}}{M_L}}$$

(6)

where K_Lt is the total cost of loading (USD/year); K_Lmr is the cost of maintenance and repairs (USD/year); K_LLab is the labor cost inclusive of salaries and wages for loading operators and technicians (USD/year); K_Lf is the cost of fuel associated with loading (USD/year); K_Leqd is the cost of equipment depreciation (USD/year); K_Lc is the cost of consumables (i.e., lubricants, spare parts, etc.) associated with loading (USD/year); K_LO is the other costs (USD/year); OPEX_L is the loading operating cost (USD/ton); K_Ltc is the total cost inclusive of the 10% contingency (USD/year); and M_L is the annual volume of material loaded (tons/year).

2.4.4. Hauling

The hauling fleet comprised 40 trucks, with three payload capacities of 40, 60, and 100 tons, a fill factor of 0.9, and an availability rate of 85%. The truck cycle time for both pit-to-waste dumping and pit-to-stockpiles/Run-of-Mine (ROM) pad routes was considered fixed at approximately 46 min based on empirical observations. Each route spans about 3.4 km, which is assumed to be constant given the almost unchanging location of the pit loading point. Annually, an average of 48,000,000 tons of material was hauled, i.e., 10,500,000 tons of ore and 37,500,000 tons of waste. This represents approximately a 9% reduction compared to the annual volume of material loaded due to compaction and minor spillage losses during hauling.

The hauling operating cost was calculated using the following equations:

$$K_{Ht}=K_{Hmr}+K_{Hlab}+K_{Hf}+K_{Heqd}+K_{Hc}+K_{HO}$$

(7)

$$OPE{X}_H={\displaystyle \frac{K_{Htc}}{M_H}}$$

(8)

where K_Ht is the total cost of hauling (USD/year); K_Hmr is the cost of maintenance and repairs (USD/year); K_Hlab is the labor cost inclusive of salaries and wages for hauling operators and technicians (USD/year); K_Hf is the cost of fuel associated with hauling (USD/year); K_Heqd is the cost of equipment depreciation (USD/year); K_Hc is the cost of consumables (i.e., lubricants, spare parts, etc.) associated with hauling (USD/year); K_HO is the other costs (USD/year); OPEX_H is the loading operating cost (USD/ton); K_Htc is the total cost inclusive of the 10% contingency (USD/year); and M_H is the annual volume of material hauled (tons/year).

2.4.5. Stockpiling

The mine followed standard practice for material storage, with 70% of the extracted ore totaling an average of 7,350,000 tons/year sent directly to the ROM pad for immediate processing, while the remaining 30% (3,150,000 tons/year) was stockpiled for future use. For waste material, 37,500,000 tons/year were dumped at the waste dump. In total, an average of 40,650,000 tons of material was stockpiled annually.

The stockpiling cost was estimated using the following equations:

$$K_{St}=K_{Smr}+K_{Slab}+K_{Sf}+K_{Sstr}+K_{Seqd}+K_{Sim}+K_{SO}$$

(9)

$$OPE{X}_S={\displaystyle \frac{K_{Stc}}{M_S}}$$

(10)

where K_St is the total cost of stockpiling (USD/year); K_Smr is the cost of maintenance and repairs (USD/year); K_Slab is the labor cost inclusive of salaries and wages for stockpiling operators and technicians (USD/year); K_Sf is the cost of fuel associated with stockpiling (USD/year); K_Sstr is the storage cost (USD/year); K_Seqd is the cost of equipment depreciation (USD/year); K_Sim is the inventory management cost of software licenses and others (USD/year); K_SO is the other costs (USD/year); OPEX_S is the stockpiling operating cost (USD/ton); K_Stc is the total cost inclusive of the 10% contingency (USD/year); and M_S is the annual volume of material stockpiled (tons/year).

2.4.6. Blending

Ore stocks were classified into three categories based on Cu grade: low grade (LG), high grade (HG), and very high grade (VG). The Cu grade ranges were defined as follows: 0.55 < LG ≤ 1.0%, 1.0 < HG ≤ 1.80%, and VG > 1.80%. The mine has adopted a standard ore blending practice, whereby 15% of the ore stockpiled at the ROM pad (1,102,500 tons) is sent directly to the processing plant unblended, while the remaining 85% (6,247,500 tons) is blended before being processed.

The blending operating cost was determined using the following equations:

$$K_{Bdt}=K_{Bdmr}+K_{Bdlab}+K_{Bdf}+K_{Bdeqd}+K_{Bdst}+K_{Bdqc}+K_{BdO}$$

(11)

$$OPE{X}_{Bd}={\displaystyle \frac{K_{Bdtc}}{O_{Bd}}}$$

(12)

where K_Bdt is the total cost of blending (USD/year); K_Bdmr is the cost of maintenance and repairs (USD/year); K_Bdlab is the labor cost inclusive of salaries and wages for blending operators and technicians (USD/year); K_Bdf is the cost of fuel associated with blending (USD/year); K_Bdeqd is the cost of equipment depreciation (USD/year); K_Bdst is the sampling and testing cost (USD/year); K_Bdqc is the quality control cost (USD/year); K_BdO is the other costs (USD/year); OPEX_Bd is the blending operating cost (USD/ton); K_Bdtc is the total cost inclusive of the 10% contingency (USD/year); and O_Bd is the volume of ore blended (tons/year).

2.4.7. Comminution

The comminution circuit included crushing and milling (or grinding). In this case study, the crushing circuit consisted of primary and secondary stages. The primary stage employed a gyratory crusher, with an average feed size (F₈₀) of 750 mm and an average product size (P₈₀) of 118 mm. In the secondary stage, a pebble crusher was used to process material with a F₈₀ in the −65 + 12 mm fraction and an average P₈₀ of 12 mm. The total specific energy consumption required for crushing was approximately 1.94 kWh/t.

Milling was conducted in wet mode. In the primary stage, a semi-autogenous grinding (SAG) mill was used to process material with an average F₈₀ of 118 mm and a P₈₀ of less than 65 mm. The secondary stage employed a ball mill with a F₈₀ greater than 150 µm and a P₈₀ in the −150 µm +2.5 mm fraction. The total specific energy consumption for milling was 11.15 kWh/t, with an operating Bond work index of 10 kWh/t.

The annual throughput of the comminution circuit reached an average of 10,350,000 tons, and considering that crushing and milling shared similar cost components, the operating costs for these processes were determined using the following equations:

$$K_{Ct}=K_{Cmr}+K_{Clab}+K_{Cenc}+K_{Ceqd}+K_{Cc}+K_{CO}$$

(13)

$$OPE{X}_C={\displaystyle \frac{K_{Ctc}}{Tph}}$$

(14)

where K_Ct is the total cost of crushing and milling (USD/year); K_Cmr is the cost of maintenance and repairs (USD/year); K_Clab is the labor cost inclusive of salaries and wages for operators and technicians (USD/year); K_Cenc is the cost of energy consumption (USD/year); K_Ceqd is the cost of equipment depreciation (USD/year); K_Cc is the cost of consumables (USD/year); K_CO is the other costs (USD/year); OPEX_C is the operating cost for crushing or milling (USD/ton); K_Ctc is the total cost inclusive of the 10% contingency (USD/year); and Tph is the throughput (tons/year). Note that each cost component is related to crushing or milling, and throughput is considered for the entire comminution circuit.

2.4.8. Flotation

The flotation circuit operated in three stages: rougher, scavenger, and cleaner. The circuit received an average annual feed rate of 28,568,816.33 tons, equivalent to an average of 3450.34 t/h. The slurry consisted of 30% solids and had a density of 1.225 t/m³.

In terms of flotation performance, 92% of copper (Cu) was recovered at an average concentrate grade of 28% Cu.

The flotation operating cost was estimated using the following equations:

$$K_{Ft}=K_{Fmr}+K_{Flab}+K_{Fenc}+K_{Feqd}+K_{Fc\mathrm{r}}+K_{FO}$$

(15)

$$OPE{X}_F={\displaystyle \frac{K_{Ftc}}{FR}}$$

(16)

where K_Ft is the total cost of flotation (USD/year); K_Fmr is the cost of maintenance and repairs (USD/year); K_Flab is the labor cost inclusive of salaries and wages for flotation operators and technicians (USD/year); K_Fenc is the cost of energy consumption relating to flotation (USD/year); K_Feqd is the cost of equipment depreciation (USD/year); K_Fcr is the cost of flotation consumables and reagents (USD/year); K_FO is the other costs (USD/year); OPEX_F is the operating cost associated with flotation (USD/ton); K_Ftc is the total cost inclusive of the 10% contingency (USD/year); and FR is the feed rate (tons/year).

3. Results

3.1. Distribution of Operating Cost Components

Based on the data in Table 1, the distribution of operating cost components across the entire mine value chain is graphically represented in this sub-section.

3.1.1. Drilling

Figure 2 visually represents the cost distribution across the various components contributing to the total cost associated with the drilling operation.

The distribution of operating cost components associated with drilling is predominantly driven by consumables, which account for 49% of the total cost. Fuel is another major contributor, making up 41%. These two components (i.e., consumables and fuel) constitute most of the expenses relating to drilling. On the other hand, maintenance and repairs, labor, and equipment depreciation are the least costly components, representing a combined 9% of the total drilling cost. These three cost components are deemed small since they collectively have a minimal impact on drilling costs compared to the consumables and fuel.

3.1.2. Blasting

Figure 3 illustrates the distribution of costs among the various components contributing to the total cost associated with blasting.

Explosives and accessories contribute 97.9% of the total blasting cost, making them the primary cost driver. In comparison, fuel, labor, permits, and regulatory costs collectively account for 1.71% of the total cost, representing the least significant cost components.

3.1.3. Loading

A graphical breakdown of all cost components associated with loading cost is provided in Figure 4.

From Figure 4, it is evident that fuel is the primary cost driver accounting for 59% of the total loading cost. Consumables are the second-largest contributor at 24%, followed by maintenance and repairs at 9% and equipment depreciation at 6%. Labor accounts for 1% of the total cost.

3.1.4. Hauling

Figure 5 shows the distribution of cost components and their contributions to the total hauling cost.

Similar to loading cost, Figure 5 illustrates that hauling cost is evenly distributed across the same primary cost components. Fuel is the largest contributor, accounting for 63.2% of the total hauling cost, followed by consumables at 14%, equipment depreciation at 10.3%, and maintenance and repairs at 8.7%. Labor represents 2.4% of the total cost associated with hauling as a unit operation of the value chain.

3.1.5. Stockpiling

A visual representation of the cost distribution for key components associated with stockpiling cost is available in Figure 6.

The estimated stockpiling cost is primarily driven by maintenance and repairs, labor, equipment depreciation, and fuel, which collectively account for 83% of the total cost. In contrast, inventory management and storage are the smallest components, accounting for 10% and 4% of the total stockpiling cost, respectively.

3.1.6. Blending

Figure 7 visually shows the distribution of costs across the various contributing components to blending costs.

As seen in Figure 7, fuel, as well as maintenance and repairs, are the largest contributors to the total blending cost, accounting for 24% and 20% of the total cost, respectively. Other components include labor and equipment depreciation, accounting for 16% and 14%, respectively. Lastly, sampling and testing and quality control are the smallest components at 12% and 9% of the total cost, respectively.

3.1.7. Comminution

Figure 8 and Figure 9 provide visual representations of the cost distribution across the various components contributing to crushing and milling costs, respectively.

In terms of crushing, energy consumption is the largest contributor; it accounts for 56% of the total cost. Consumables and maintenance and repairs follow at 22% and 9%, respectively. In comparison, labor, as well as equipment depreciation, are amongst the smallest portions of the total crushing cost at 6% and 5%, respectively.

From Figure 9, energy consumption is seen to account for 59.5% of the total milling cost. Consumables are a close second at 35.7%, followed by maintenance and repairs at 1.9%. Labor, as well as equipment depreciation, are the smallest cost components at 1.5% and 0.9%, respectively.

3.1.8. Flotation

A visual breakdown of all cost components associated with flotation is provided in Figure 10.

As shown in Figure 10, consumables and reagents contribute the most to flotation cost, representing 79.6% of the total cost. Energy consumption is also significant, at 11.5%, while labor and maintenance and repairs, as well as equipment depreciation, collectively contribute 6.9%.

3.2. Estimation of Operating Costs

The results of estimating operating costs across the entire mine value chain, using Equations (1)–(16), are summarized in

Table 2. Summary of all operating costs characteristic of the mine value chain in USD/t and percent fraction.

Category	Operation	Parameter	Cost (USD/Year)	OPEX (USD/t)	%
		KDt	13,977,120
	Drilling	Contingency	1,397,712	0.35	3.35
		KDtc	15,374,832
		KBt	22,062,530
	Blasting	Contingency	2,206,253	0.44	4.21
		KBtc	24,268,783
		KLt	24,433,800
	Loading	Contingency	2,443,380	0.51	4.88
		KLtc	26,877,180
		KHt	41,041,953.46
Mining	Hauling	Contingency	4,104,195.35	0.94	8.99
		KHtc	45,146,148.81
		KSt	764,910.81
	Stockpiling	Contingency	76,491.08	0.02	0.19
		KStc	841,401.89
		KBdt	349,775.42
	Blending	Contingency	34,977.54	0.06	0.57
		KBdtc	384,752.96
	Total			2.32
		KCt	10,825,104.77
	Crushing	Contingency	1,082,510.48	1.15	10.99
		KCtc	11,907,615.25
		KCt	58,175,061.51
Processing	Milling	Contingency	5,817,506.15	6.18	59.08
		KCtc	63,992,567.66
		KFt	21,080,648.06
	Flotation	Contingency	2,108,064.81	0.81	7.74
		KFtc	23,188,712.87
	Total			8.14
	Total OPEX			10.46	100

. Additionally,

Table 3. Comparison of operating costs between the case study and industry benchmarks.

Category	Operation	OPEX		Benchmarks (%)	References
		(USD/t)	(%)
	Drilling	0.35	15.09	8–15	[31,40,41]
	Blasting	0.44	18.97	10	[31,42]
Mining	Loading	0.51	21.98	9–16	[25,30,41,42,43]
	Hauling	0.94	40.52	43–70	[30,41,44,45,46,47]
	Stockpiling	0.02	0.86	0.05–0.25	[48]
	Blending	0.06	2.59	0.5–3	[48,49]
	Total	2.32	100
	Comminution	7.33	90.05	40–50	[50,51]
Processing	Flotation	0.81	9.95	15–20	[52,53,54,55]
	Total	8.14	100
	Total OPEX	10.46

presents a comparison of operating costs between the case study and the industry benchmarks.

As shown in Table 2, the breakdown of operating costs indicates that milling is the largest contributor to total OPEX, followed by crushing and haulage, highlighting the cost distribution across the open-pit copper mine value chain studied.

3.3. Distribution of Operating Costs

Figure 11, Figure 12, Figure 13 and Figure 14, derived from Table 2 and Table 3, illustrate the allocation of operating costs across the mine value chain while highlighting the contribution of mining and processing units.

In Figure 11, the operating cost breakdown reveals that 59.08% of the overall OPEX is attributed to milling, followed by crushing at 10.99%. This means that approximately 70% of the overall OPEX is associated with comminution alone.

Zooming in on mining operations in Figure 12, one can see that hauling constitutes the largest share of mining OPEX at 40.52%, followed by loading at 21.98%. Blasting accounts for 18.97%, while drilling represents 15.09%. Blending contributes 2.59%, while stockpiling is the smallest portion at 0.86%.

Similarly, Figure 13 unpacks processing costs. It is evident here that milling is the largest cost driver, accounting for 75.92% of the total processing OPEX. Crushing follows with 14.13%, while flotation represents the smallest share at 9.95%.

In summary, Figure 14 illustrates the distribution of operating costs associated with the open-pit copper mine investigated. It can be seen that the distribution is heavily skewed towards processing operations, accounting for 77.82% of the total cost, while mining operations contribute the remaining 22.18%.

4. Discussion

This study offers a comprehensive analysis of the distribution of operating costs along the value chain of an open-pit copper mine, with particular emphasis on the contribution of various operational units. Results indicate that mineral processing, particularly comminution, represents the largest share of operating costs, with milling alone accounting for 59.08% of total operating costs. Crushing is the second-largest contributor, representing 10.99% of the overall costs. In essence, comminution (i.e., crushing and milling) accounts for nearly 70% of total OPEX. These findings are consistent with previous studies that have consistently highlighted comminution as one of the most expensive stages in mining operations [3,50,56,57]. This is primarily due to the energy-intensive nature of the process, coupled with its relatively low operational efficiency [58,59].

In terms of mineral processing operations, comminution constitutes 90.05% of the total mineral processing OPEX (Table 3), substantially exceeding the industry benchmark range of 40–50% [50,51]. This discrepancy can be attributed to the fact that only comminution and flotation have been considered as mineral processing units in this study, while other key operations, such as dewatering, tailings disposal, and extractive metallurgical processes, were excluded from the analysis [54]. As a result of this, the contribution of comminution to mineral processing OPEX appears disproportionately high, underscoring the energy-intensive nature of milling and crushing operations. The substantial energy demands of comminution emphasize the need to prioritize energy-efficient grinding technologies, including high-efficiency equipment and advanced comminution methods [60,61,62]. Also, the integration of renewable energy sources such as solar or wind can further reduce energy consumption while improving operational efficiency and sustainability, ultimately leading to lower overall costs [63,64,65,66,67].

Looking at flotation, this operation accounts for 7.74% of the total OPEX of the open-pit mine value chain and plays a crucial role in copper recovery. Automating flotation processes and optimizing reagents usage could further enhance recovery rates, improve profitability, and reduce OPEX. This would then contribute to enhancing the overall efficiency of the mine [68]. With flotation representing 9.95% of total processing OPEX, its contribution is deemed low compared to the published industry benchmark range of 15–20% [52,55]. This may suggest that the flotation process in this case study is more cost-efficient compared to the average value reported in the literature.

Drilling and blasting, which contributed 3.35% and 4.21% to the total OPEX, respectively, may seem modest in terms of their individual cost shares. However, they play a critical role in determining the efficiency of all subsequent processes, including loading, hauling, crushing, and milling. The literature suggests that drilling and blasting can account for up to 15% and 10% of total mining costs, respectively [31,40,41,42]. This case study shows that drilling and blasting account for 15.09% and 18.97% of mining OPEX, respectively. The drilling cost is at the higher end of the industry benchmark range of 8–15%, which seems consistent with typical variations in drilling costs, especially in harder rock environments. The higher blasting OPEX of 18.97% is primarily driven by the significant expenses associated with explosives and accessories, making up approximately 97.9% of the total blasting cost. Implementing mine-to-mill optimization strategies, particularly by increasing blasting intensity, may raise blasting costs in the short term. Nevertheless, this can lead to substantial savings downstream through improved ore fragmentation and reduced energy required for the crushing and grinding processes [32].

Other mining operations, such as loading, hauling, stockpiling, and blending, account for 4.88%, 8.99%, 0.19%, and 0.57% of the total OPEX, respectively. While these operations are less energy-intensive compared to comminution, hauling stands out as a significant cost contributor. In this study, hauling accounts for 40.52% of the mining OPEX, which is lower than the typical range of 43–70% reported in the literature for surface mining operations [30,41,44,45,46,47]. This difference can be explained by the short haul distances between the pit and processing facilities, resulting in lower fuel consumption and maintenance costs. Despite this, hauling still represents a considerable portion of the total OPEX and is heavily influenced by factors such as haul distances, terrain, and fleet configuration. On the other hand, loading represents 21.98% of the total mining OPEX, which exceeds the industry benchmark range of 9–16% [25,30,41,42,43]. This higher cost could suggest inefficiencies that may be addressed through improved equipment utilization, optimized fleet management, or better labor allocation. Stockpiling, accounting for 0.86% of the total mining OPEX, is above the benchmark range of 0.05–0.25% [48], possibly reflecting higher costs associated with specific stockpile management practices or operational factors at the mine. Blending, at 2.59% of the total mining OPEX, falls within the industry benchmark range of 0.5–3% [48,49], suggesting that this operation is operating efficiently in line with industry standards.

This study provides valuable insights into the distribution of operating costs along the value chain of an open-pit copper mine, with a particular focus on the energy-intensive nature of comminution, which accounts for nearly 70% of the total OPEX. The findings highlight opportunities for cost reduction through the adoption of energy-efficient technologies, mine-to-mill optimization, and renewable energy integration. By offering a detailed case study of cost distribution, this research contributes to the existing literature, enabling industry professionals to make data-driven decisions aimed at improving operational efficiency, reducing energy consumption, and enhancing sustainability. However, the study is limited by its exclusion of certain mineral processing stages, such as dewatering, tailings disposal, and extractive metallurgical processes, as well as external factors like transportation, environmental management, and reclamation. Additionally, its focus on a single case study may limit the generalizability of the findings.

From a practical perspective, the research provides actionable strategies for improving cost-efficiency in mining operations. By optimizing energy use and refining operational practices, mining companies can significantly reduce costs, increase profitability, and enhance sustainability. While these findings offer valuable insights for mining engineers to make informed, data-driven decisions for enhanced efficiency and sustainability, future research should consider additional processing units, such as dewatering, tailings disposal, and extractive metallurgy, as well as external factors like transportation, environmental management, and reclamation, to offer a more comprehensive understanding of cost distribution across the entire mining value chain.

5. Conclusions

This study aimed to provide a detailed analysis of the distribution of operating costs along the value chain of an open-pit copper mine, with a particular focus on identifying key areas for cost reduction and their implications for operational efficiency and strategic decision-making in mine planning.

The following findings emerged from the analysis:

• Comminution, particularly milling, accounted for 59.08% of total OPEX, with crushing representing an additional 10.99%. Together, these units accounted for nearly 70% of total OPEX, highlighting comminution as one of the most energy-intensive and costly operations in mining. Furthermore, flotation accounted for 7.74% of total OPEX and 9.95% of mineral processing OPEX;
• Drilling and blasting contributed 3.35% and 4.21% to total OPEX, respectively. Additionally, drilling accounted for 15.09% of total mining OPEX, and blasting OPEX was notably high, representing 18.97% of total mining operating costs, exceeding the industry benchmark associated with blasting OPEX, primarily due to the significant expenses related to explosives and accessories;
• Hauling accounted for 8.99% of total OPEX. In addition, it represented 40.52% of total mining OPEX, slightly lower than the typical industry benchmark range for surface mining operations unlikely due to short transport distances between mine and processing facilities, resulting in lower fuel consumption and maintenance costs. In contrast, loading represented 4.88% of total OPEX and 21.98% of total mining OPEX;
• Other mining operations, such as stockpiling, accounted for 0.19% of total OPEX and 0.86% of mining OPEX. Blending contributed 0.57% of total OPEX and 2.59% of total mining OPEX.

This study provides valuable insights into the distribution of operating costs across the value chain of an open-pit copper mine, specifically focusing on internal operational costs from blasting to flotation. By identifying key cost drivers within mining operations, the paper offers a detailed cost breakdown that can inform strategic mine planning and resource allocation decisions, ultimately contributing to improved operational efficiency and sustainability.

The findings also contribute to the literature by providing real-world data on cost distribution in a sedimentary-hosted copper mine, filling a gap in existing research on cost distribution for strategic mine planning. This research is particularly relevant for mine operators, financial analysts, and decision-makers aiming to optimize operational costs while ensuring sustainable resource utilization.

Finally, this research paper lays the foundation for future studies that should consider the following:

• Incorporating additional mineral processing units, such as dewatering, tailings disposal, and extractive metallurgy, as well as external factors like transportation, environmental management, and reclamation for a more comprehensive description of costs along the value chain;
• Conducting a sensitivity analysis on energy costs, equipment efficiency, and production rates to enhance the robustness of the current findings, especially under varying operational and market conditions;
• Extending the analysis to other commodities and geographical locations for a generalized profile of cost structures available in various surface mining operations;
• Exploring the potential impact of renewable energy integration, automation, and mine-to-mill optimization strategies on cost structures to identify new avenues for enhanced operational efficiency and reduced costs.