The Influence of the Mining Operation Environment on the Energy Consumption and Technical Availability of Truck Haulage Operations in Surface Mines

Abstract

This paper presents an analysis of the impact of selected parameters of the operating environment on the energy consumption and reliability of haulage in surface mining. The analysis is based on a cyclic haulage system in a limestone open pit. The results of the calculations show that maintaining the operating environment in good technical condition has a positive effect on the haulage process and a direct or indirect effect on the operating costs, the analysis of which is also presented in the main body of the article. The analysis was carried out for a full year’s production, taking into account actual operating and maintenance downtime. The results of similar analyses can be used as a basis for deciding on the type of truck to be used for transport or for reconfiguring transport routes. In addition to the economic and operational aspects of energy consumption and reliability, the environmental aspect cannot be overlooked. The comparison of two variants of mining conditions shows that a modification of the haul road surface leads to a significant reduction in fuel consumption. Depending on the type of vehicle, fuel consumption can be reduced by almost 20%. The potential reduction in fuel consumption directly translates into lower exhaust emissions, which is an important element of an environmentally sustainable approach to mining transport, and greater reliability increases efficiency and reduces the carbon footprint of the vehicle.

1. Introduction

Mining trucks are the dominant means of transport in open-cast mines and quarries, and to date the economics of haulage have been closely linked to oil consumption and fuel price fluctuations. A rational choice of mine haulage system requires a thorough analysis of all operational parameters. When selecting the haulage system to be used or assessing its performance, most users tend to overlook some important parameters, including its energy efficiency in relation to the mining environment: the haulage road system and the potential impact of operating conditions on the performance and failure rate of mine trucks.

In addition to the economic aspect related to fuel consumption, environmental issues are of paramount importance. A potential reduction in fuel consumption will result in lower greenhouse gas emissions, which is essential as part of a sustainable approach to mine haulage in the light of current environmental requirements. In addition, little consideration is given to the impact of the mine operating environment, i.e., the configuration, type and working conditions of haul roads on truck performance and machine failure rates.

Taking into account all the key parameters of the truck, including the tare weight to payload ratio and powertrain efficiency, as well as the properties of the haul roads, energy consumption relationships were determined. This includes the amount of energy required to overcome all resistance to the vehicle’s movement and to transport a unit weight of load per unit distance. A relationship has also been established for the specific fuel consumption of technological vehicles, taking into account all relevant vehicle parameters and the transport route. Using these relationships, analyses were carried out for two types of vehicle to illustrate the variability of energy consumption depending on vehicle parameters and operating conditions. In the total operating costs of raw materials, transport costs are the dominant component, so knowledge of specific energy consumption is essential for the rational selection of means of transport for specific operating conditions.

The unit fuel consumption of mine trucks was determined in relation to the key operational and technical parameters. The energy consumption and selected reliability parameters of two means of transport were examined while taking into account the conditions in the existing mining operation environment. This paper proposes specific modifications to the mining environment and explores their potential for energy consumption reduction. The derived dependencies were used in the performance analysis of two types of mine trucks widely operated in Europe: an articulated hauler and a four-axle mine truck.

The fuel consumption of mining trucks depending on different parameters is described by Kecojevic, V., et al. [1,2] and Curi, A., et al. [3]. Bodziony P. et al. [4] describe the use of multi-criteria methods in the selection of trucks. Sahoo, L. K., et al. [5] described a comparative analysis of energy consumption in haulage. Krysa, Z, et al. [6] presented discrete simulations in analyzing the effectiveness of haul in mining. Golbasi, O., and Kina, E. [7] described haul truck fuel consumption modeling under random operating conditions.

Analysis of fuel consumption in a large surface mine was described by Dindarloo, S. R., and Si-ami-Irdemoosa, E. [8]. Straka et al. [9] carried out a long-term analysis of haulage over half a year’s operation in a mine. The impact of payload utilization on fuel consumption has been described by Soofastaei, A., et al. [10,11], and Soofastaei, A., et al. [12] have also described the improvement of energy efficiency of haul trucks in open pit mines. The use of models based on neural networks or machine learning to reduce fuel consumption in mining transport has been described by Siami-Irdemoosa, E., and Dindarloo, S. R. [13] and also Bousonville, T., et al. [14] and similarly Terpstra, V. J., [15]. Alamdari, S., et al. [16] presented the application of machine learning to predict fuel consumption of haul trucks in open pit mines.

The issue of haul roads has been simulated and analyzed in [17], where geotechnical tests were used to provide a mine with an optimal transport solution. The impact of haul roads has also been described by Douglas, R. A., and Lawrence, K. [18], Richardson, S., and McIver, J. [19] as well as Coffey, J., Nikraz, H., and Leek, C. [20,21]; Li, X., et al. [22]; and Hasan, H., and Octariando, R. [23]. The use of machine effectiveness evaluation metrics described by Antosz, K., and Stadnicka, D. [24]. A mixed model proposed by Senderova, J., et al. [25] to optimize machine selection to reduce costs while reducing greenhouse gas (GHG) emissions. A reduction in the mining footprint by adapting the haulage fleet is described in [26]. The impact of reliability on the energy consumption and greenhouse gas emissions of the mining haulage fleet is described by Peralta, S., et al. [27]. Analyses of equipment failure in mining were described by Hall, R. A., et al. [28], Yu, H., et al. [29] and Paithankar, A., and Chatterjee, S. [30]. Morad, A. M., et al. [31] analyzed the machine failure rate using the Monte Carlo method. Monte Carlo simulation was also used in an analysis of the performance of technological processes and the transport road network [32]. In turn, preventive maintenance of mining equipment was described by Angeles et al. [33], and Topal [34] et al. drew attention to the high operating costs associated with the use of haulage given the age of the machines and their availability. Similar analyses were presented by Kristjanpoller, F., et al. [35]. Articles present analyses of the development of haulage in open pit mining: Feng, Y., et al. [36], Kawalec, W., et al. [37], Purhamadani, E., et al. [38] and Bao, H., et al. [39].

In the literature review conducted, no position was found that simultaneously analyzes the impact of the operating environment on the energy intensity of transport and its reliability. The authors’ previous analyses have treated these aspects separately, so this paper attempts a multifaceted analysis of the issue of the impact of the operating environment on the means of transport, introducing the aspect of operating costs.

2. Materials and Methods

The analysis of truck haulage operations in a surface mine is divided in two parts: the analysis of energy intensity (energy consumption) in relation to the real mining operation environment and the analysis of the failure rate, taking into account the real operating conditions in the mine.

• Energy consumption of the truck haulage as a parameter in the performance evaluation

When analyzing the movement of a haul truck as a sequence of energy processes, of particular importance is the method of supplying energy required for the processes, i.e., energy transfer from the engine via the drivetrain to the wheels and utilization of available kinetic energy of the vehicle (built up in the acceleration phase) to overcome the resistance to motion (due to rolling and tilting) during the decelerated motion. During each phase of the mine truck ride, cycle energy is expended to perform the haulage work. From the technical and operational parameters of analyzed vehicles and those associated with the existing mining operation environment (road configuration, road sloping, coefficient of rolling resistance), the unit energy consumption and the unit mass of fuel used can be derived. Unit energy consumption is expressed in terms of work to be performed by the haul truck to overcome all the resistances to motion when traveling the specified distance with the specified velocity.

The analysis of the energy consumption of the truck’s transport was based on the resistance of the vehicle in motion, excluding the energy consumption associated with the idling of the engine during the downtime at the loading and unloading points, which was considered insignificant. The analysis presented here takes into account all the relevant components of the energy intensity of steady-state movement of a vehicle, ignoring the energy intensity associated with the engine idling at loading and unloading points, as this value can be treated as a constant value irrespective of the type of vehicle analyzed and does not affect their comparison in steady-state movement. The efficiency of the engine and driveline system, as well as the hydraulic system for lifting the cargo box, were also taken into account. The analysis was based on the assumption that the excavated material is transported by truck on a road of length L and with a total angle of inclination (determined from all inclined sections of the transport road) relative to the horizontal. During a single haulage cycle of the truck ride between the loading and unloading stations, the energy consumption is expressed in terms of work performed by the vehicle (1):

$$\begin{array}{c}E=L\left[\left(G_u+G_0\right)\left(f\cos \alpha +\sin \alpha \right)+G_0\left(f\cos \alpha -\sin \alpha \right)\right]+\left(\frac{G_u+2G_0}{2g}+2L{k}_x{F}_s\right)v^2+G_u{h}_c\\ =L\left[\left(G_u+{2G}_0\right)f\cos \alpha +G_u\sin \alpha \right]+\left(\frac{G_u+2G_0}{2g}+2L{k}_x{F}_s\right)v^2+\frac{G_u{h}_c}{{\eta }_c},\left[J\right]\end{array}$$

(1)

Unit energy intensity of the vehicle $e_j$ used as work, related to the unit weight of excavated material transported by haul road 1m $(\frac{J}{Nm}=\frac{J}{J}=\frac{MJ}{tkm}\cdot \frac{s^2}{m}$ dimensionless quantity) after taking into account relation (1), has the form:

$$e_j=\frac{E}{G_{u}L}=\left(1+2\frac{G_0}{G_u}\right)f\cos \alpha +\sin \alpha +\left[\left(1+2\frac{G_0}{G_u}\right)·\frac{1}{2gL}+\frac{2k_x{F}_s}{G_u}\right]·v^2+\frac{h_c}{L{\eta }_c}$$

(2)

where:

• G₀—weight of the truck when empty, [N].
• G_u—loading capacity, [N].
• k_x—coefficient of air resistance (k_x = 0.9–1.2 for mine trucks).
• f—coefficient of rolling resistance.
• v—nominal velocity, [m/s].
• α—summary haul road sloping, [°].
• $F_s$—front surface of the vehicle, [m²].
• L—distance between the loading and unloading point, [m].
• h_c—vertical rise of the center of gravity (cog) of the truck box with the payload, [m].
• η_c—coefficient of performance of the body lifting system, [%].

Relationship (2) defines the two major components of the energy input during the truck haulage operations required to overcome the resistances to motion due to:

• Rolling resistance:

  $$e_f=\left(1+2\frac{G_0}{G_u}\right)f\cos \alpha$$

  (3)
• Grade resistance:

  $$e_{\alpha }=\sin \alpha$$

  (4)
• Reaching the required ride velocity:

  $$e_v=\left[\left(1+2\frac{G_0}{G_u}\right)·\frac{1}{2gL}+\frac{2k_{x}F}{G_u}\right]·v^2$$

  (5)
• Lifting the box with the material hauled:

  $$e_h=\frac{h_c}{L{\eta }_c}$$

  (6)

From relations (1) and (2), it can be concluded that in order to reduce the energy consumption of a vehicle, and therefore fuel consumption (and exhaust emissions), it is necessary to ensure the lowest possible value of the coefficient of drag f, i.e., the hardest possible surface of the haulage road, without longitudinal and lateral irregularities, the use of access roads with low gradients, as well as the use of vehicles with a low value of the parameter G₀/G_u (favoring the ratio of empty weight to payload) and an optimum traffic speed v.

Considering the constructional design of the vehicle, efficiency of the drivetrain and high-pressure engine as well as the calorific value of diesel fuel and recalling the relationship 1litre of diesel fuel = 0.84 kg and formula (2), the unit mass of fuel required to perform the haulage operation can be given as (7):

$$m_{jp}=\left(\frac{1.25}{W_d·{\eta }_s·{\eta }_{un}}·\left(1+2\frac{G_0}{G_u}\right)fcos\alpha +sin\alpha \left.+\left[\left(1+2\frac{G_0}{G_u}\right)·\frac{1}{2gL}+\frac{2k_{x}F}{G_u}\right]·v^2+\frac{h_c}{L·{\eta }_c}\right)·g\right.,\left[\frac{l}{tkm}\right]$$

(7)

where:

• W_d—calorific value of fuel oil, W_d = 43 MJ/kg.
• η_s—engine efficiency, [%].
• η_un—drivetrain efficiency, [%].
• η_c—coefficient of performance of the body lifting system, [%].

The efficiency of the diesel engine and the efficiency of the driveline also cause the efficiency of the driveline to vary with the moment load of those assemblies that process and transmit energy from the engine to the wheels, i.e., gearboxes, reducers and other transmissions. Therefore, in the case of variable speed driving on a horizontal road, the efficiency of the driveline takes on instantaneous values that depend indirectly on the speed profile achieved, as can be seen from relation (1). On the other hand, from relation (2) it can be seen that the fuel consumption of the vehicle, and therefore its energy consumption, is also influenced by the efficiency of the engine and the whole powertrain. The efficiency of the engine varies over a much wider range (depending on the phase of movement—acceleration or constant speed) than the efficiency of the transmission. It is therefore important to consider these parameters as a factor with a significant influence on the average efficiency of the entire powertrain and, consequently, on fuel consumption.

• Haul truck reliability parameters in the comparative analysis

The comparative analysis was based on the following reliability parameters:

• Reliability index K_gt—reliability parameter expressing the probability that the equipment is operational and running at a given time. This criterion is determined for each type of equipment in the truck fleet and defined as the mean value for each type of machine. The value of the reliability index is expressed in [%] (8).

$$K_{gt}=\frac{{\sum }_{i=1}^n{t_i}^{\left(j\right)}}{{\sum }_{i=1}^n{t_i}^{\left(j\right)}+{\sum }_{i=1}^n{t_i}^{\left(n\right)}}$$

(8)

where:

• t_i^(j)—machine’s effective operating time in i-th day of operation [h].
• t_i⁽ⁿ⁾—total repair and maintenance time, including (9):

$$\sum _{i=1}^n{t_i}^{\left(n\right)}=t_r+t_s+t_{pw}$$

(9)

where:

• t_r—effective repair downtime [h].
• t_s—repair time by an external/third party service [h].
• t_pw—workshop downtime [h].

The reliability index was determined as an average value for the two groups of analyzed haul trucks over 12 months of operation, which corresponds to about 4100 mth (hours of operation).

• Failure rate/failure intensity function.

The cumulative failure intensity function λ(t) was formulated, and the Weibull distribution density was employed based on references [40,41,42,43].

The distribution of failure intensity was determined for all machines based on service requests submitted to the restoration system. These requests included serious failures as well as minor defects indicated and detected by the telemetry system or the operators. The Weibull distribution was also used to model unplanned maintenance downtime. When defining the failure intensity distribution, the mean number of events per unit of time was assumed to change with time, and in such case the number of defective objects was defined and the time to failure was random.

• Mine trucks considered in the comparative analysis

The comparative analysis was conducted for two types of mine trucks with similar technical and operational parameters. The analyzed fleet consisted of two groups of 4 vehicles operated in the similar working condition and with similar mileage (new machines, newly put into operation). In the analyses below, it is assumed that the test conditions in both configurations of the operating environment are the same and that the haul trucks are in the same technical condition. Operational and technical parameters of the analyzed trucks are summarized in

Table 1. Parameters of mine trucks used in the calculation procedure.

Parameter	Bell B40E	Tatra T158
G0 [N]	316,206	133,220
Gu [N]	382,590	294,300
kx [-]	1.0	0.9
η * [%]	75	80
F [m2]	13.26	8.0
hc [m]	3.27	3.5
v [m/s]	5	5

* Total efficiency of the drive train.

.

• Influence of the existing mining operation environment (OE I) on truck haulage operations.

As a part of the comparative analysis, in situ measurements were taken of the resistance to motion encountered by trucks on the main haul road in a surface mine based on the 3D model of the mine shown in Figure 1.

Mine sections with different road surface types are highlighted with colors, and the corresponding coefficients of rolling resistance are given in the lower right corner (Figure 2), revealing the indicated section of the main haul road. The longitudinal road profile obtained was 1850 m in length from level 370 and the loading station right through to the stationary processing plant designated by K in Figure 3.

3. Results of Analysis of the Unit Mass of Fuel Consumption for the Existing Mining Operation Environment

Real parameters of the mining operation environment and technical and operational specifications of mine trucks were used in the calculation procedure, yielding the plots of m_jp in the function of the haul road length, its sloping angle and the coefficient of rolling resistance.

Components of the unit energy consumption intensity (2) have different impacts on the energy expenditure that the engine of the haul truck has to cover while performing the transport task. The influence of the last two components of the unit energy intensity in relation (10) is much smaller, as the first two components are dominant. It should be noted that the component e_f ≥ e_α for:

$$(1+2\frac{G_0}{G_u})fcos\alpha \ge sin\alpha \mathrm{while}:\alpha \le arctg(1+2\frac{G_0}{G_u})f$$

(10)

As can be seen from the Figure 4 and relationship (10), the unit energy intensity of a haul truck is therefore mainly influenced by the condition of the road surface (f-factor); the G_o/G_u quotient; the gradient of the haul road; and, after taking into account the vehicle parameters, the efficiency of the transmission system, as analyzed below.

Relationships between the analyzed formula (7) capturing the mining operation environment for the mine trucks being subject to the comparative analysis considering vehicle parameters and the operating environment are plotted in Figure 5, Figure 6 and Figure 7.

As regards the m_jp dependence on the haul road length (Figure 5), it appears that the value of unit mass of fuel consumed tends to decrease with increasing haul road length. Thus, for the value m_jp(L) starting from L approximately equal to 800 m (a one-way operation), road transport becomes the favorable option because the energy input and the m_jp value remains stable for equal or larger haulage distances.

Moreover, a mine truck traveling on a road with effective grade experiences load due to its weight component, associated with the road grade. The larger the sloping angle of respective road segments (Figure 6), the larger the magnitude of the grade resistance. In order to maintain the constant speed during the ride, the driver has to increase the driving power, which results in an increase in the energy input required to perform the haulage work and, consequently, of the unit mass of consumed fuel. A similar pattern is observed for the rolling resistance m_jp(f) (Figure 7), showing that in order to reduce the energy consumption of the mine trucks, it is recommended that haulage roads should be designed and constructed with the best possible road surface, made of long, gently sloping road segments (instead of short, steep ones). Differences in the values of unit energy consumption and unit mass of fuel consumed for the analyzed trucks are attributable to differences in their technical parameters, such as the powertrain efficiency and the truck’s ratio of empty weight to payload capacity.

4. Results of Analysis Reliability Parameters of Mine Trucks in Operation Environment (OE I)

Comparative performance analysis of two types of mine trucks was based on the availability index and the failure intensity function in the existing mining operation environment, considering the impacts it has on machine reliability. The causes of faults and defects were examined based on data and reports provided by the mining company.

In the analysis of the reliability parameters carried out, reference was made to failures resulting from sudden, unexpected failure or improper operation of the vehicle, leading to its stoppage or loss of ability to function normally, resulting in unplanned maintenance. Planned inspections and maintenance (derived from technical and operational documentation) were not taken into account.

Calculated values of the (8 and 9) K_gt index are summarized in

Table 2. Calculated values of the K_gt index during the one year.

		Bell B40E			Tatra T158
	ti(j)	ti(n)	Kgt	ti(j)	ti(n)	Kgt
January	210	18	92%	204	42	83%
February	222	18	93%	222	54	80%
March	258	18	93%	228	60	79%
April	204	48	81%	228	48	83%
May	222	6	97%	168	60	74%
June	246	6	98%	132	120	52%
July	252	12	95%	234	48	83%
August	210	54	80%	222	42	84%
September	216	30	88%	216	42	84%
October	240	12	95%	228	72	76%
November	234	6	98%	216	54	80%
December	234	12	95%	168	78	68%
	Average availability		92%	Average availability		77%

.

The average availability of the Bell B40E trucks is 92%, and in the case of Tatra T158 trucks it is 77% (only 52% in June!), derived as the average of values registered over a 12-month period. According to the data reported by the mining company, in most cases, the reasons why the trucks were out of service were the overhauls and corrective maintenance procedures, including the tire servicing, necessary due to failures and defects induced by the mining operation conditions, particularly in the case of heavy trucks. The impacts on truck availability, particularly of four-axle vehicles, are largely due to haul road quality, inadequate for such vehicles that are likely to sustain more severe damage than vehicles specifically designed and constructed to operate in hard conditions.

5. Results of Analysis Haul Truck Failure Intensity Function in Operation Environment (OE I)

In order to reconstruct the real operating conditions of machines over longer periods, the operating times of the road transport system (haulers and articulated trucks) were examined, and failure processes were highlighted. The reliability analysis of the vehicle fleet was conducted over a one-year timeframe, assuming a total of 4 thousand operating hours. This analysis was based on the failure rate of the truck fleet, accounting for the cumulative operation time. Studies of failure processes relied on the generated distribution patterns best fitting the data yielded by the reliability analysis, specifically the Weibull probability density function (see Figure 8 and Figure 9).

Analyzing the plots of the failure intensity function λ(t), it appears that for comparable vehicles the running-in accounts for about 1000 h of operation. It is estimated that after that running-in period, the trucks are ready for standard duty. In the case of Bell B40E articulated trucks and Tatra T158 heavy goods vehicles, the probability of a failure tends to increase following the similar pattern, though the failure intensity is found to be higher for the Tatra T158 vehicles. This can be attributable to the harsh operating conditions, which are responsible for operation shutdowns in the early period of truck operation.

The failures recorded were largely due to the adverse effects of the operating environment on the vehicles analyzed, taking into account the initial phase of vehicle operation (running-in period). The review of failures in the reporting system identified damage to tires (Figure 10), suspension systems and the powertrain as a result of use in harsh operating conditions.

6. Modifications to the Existing Mining Operation Conditions and Their Impacts on the Energy Intensity of Haulage and Unit Mass of Fuel Consumed

Following the analysis of the existing mining operation conditions and dedicated formulas, the second variant of the mining operation conditions (OE II) was considered, involving the modification of process parameters through the change in the road surface type. The proposed variant II (OEII), adopted as theoretical optimization, is shown in Figure 11.

The analyzed road segment indicated by green color is the main section of the haul road leading to respective levels, and it should remain unchanged throughout the entire period of progressing mining operations. Knowing in which direction the mining operation should advance (the north-east direction), it was reasonable to alter the parameters of the road surface made of asphalt mix. Furthermore, a fragment of the haul road was paved with concrete slabs, to be replaced when worn or broken. In the modified variant, the road surface on level 370 to the loading station was upgraded through soil compaction and stabilization with the debris material from the mine. The theoretical variant provides only for reconfiguration of the road surface while maintaining the original longitudinal profile of the site (Figure 12).

The effects of modifications to the mining operation conditions related to the unit mass of fuel consumed for the mine trucks being compared in the existing and modified mining operation conditions (variants OE I and OE II) are plotted below (Figure 13).

The comparison of two variants of the mining operation conditions (OE I and OE II) reveals that modification of the haul road surface will result in a significant reduction in fuel consumption. The difference between m_jp values registered in variant OE I and OE II amounts to 16.96% for the Bell B40E trucks and 14.75% for Tatra T158 vehicles, and more favorable values are obtained for the modified road surface.

In order to verify the adequacy and reliability of the index m_jp when analyzing the energy efficiency of road transport operations, the m_jp values are compared with the actual fuel consumption figures obtained in the variant OE I, the data being supplied by the mining company. They are collated in

Table 3. Correlation between the actual fuel consumption and the m_jp value obtained for OE I.

Truck Model	Actual Fuel Consumption [L/tkm]	Predicted Value of mjp [L/tkm]	r-Pearson Correlation Coefficient
Bell B40E	4.34	3.39	0.869
Tatra T158	3.60	2.89

alongside the r-Pearson correlation coefficient.

With actual data provided by the mining company related to the calculated unit mass of fuel consumed, the r-Pearson correlation coefficient becomes 0.869, which is indicative of very good correlation of results and proves the adequacy of the adopted analytical procedure and parameters.

7. Analysis of the Structure of the Operating Costs of Mining Trucks in Operation Environment OE I and OE II

Based on the mine’s data, the cost structure for the compared vehicles in the current operating environment (OE I) is presented below, taking into account maintenance costs resulting from the need to maintain the technical readiness of the vehicles analyzed, fuel consumption costs, leasing costs and driver salaries, i.e., all components of the operating costs of the haulage system related to the tonnage of ore transported (Figure 14).

Leasing costs and driver salaries are fixed costs, while fuel consumption and operating costs are variable costs depending on the operating environment, which can be configured. Fuel costs are a direct result of road resistance and show the greatest variability and dependence on the condition and type of pavement and the configuration of the haul roads (amount of slope and number of ascents and descents).

By improving operating conditions, it is possible to significantly reduce fuel consumption, as shown in the analysis presented here, as well as increasing machine reliability, which in turn reduces operating costs (spare parts and maintenance).

The economic analysis was based on the assumption of a reduction in maintenance costs and fuel consumption as a result of changes in the operating environment (OE II), with the assumption that the reliability and technical readiness of the vehicles would increase. The structure of technical maintenance costs was estimated, assuming an increase in technical readiness of 10% for the Bell B40E and 20% for the Tatra T-158. This assumption was made on the basis of damage characteristics resulting from current operating conditions, with some tire damage being excluded from the compilation. The reduction in fuel costs, on the other hand, results directly from the analysis of energy consumption and the unit mass of fuel required for OE II. Leasing costs and driver salaries remain unchanged. As before, all transport system operating costs are related to the tons of ore transported. The results of the analysis are shown in Figure 15.

Assuming an increase in reliability and availability of 10% for the Bell B40E and 20% for the Tatra T-158, and despite the significant difference in fuel consumption between the trucks, the Bell B40E has a lower unit cost (EUR/Mg). For the Tatra T-158, a change in the operating environment can reduce unit costs by 15.5% (EUR 0.15/Mg), while for the Bell B40E it can reduce unit costs by 8.6% (EUR 0.06/Mg). However, as the change in operating environment is more significant for the Tatra T-158 than for the Bell B40E, the Bell B40E trucks will be more economically advantageous and their design assumptions more favorable under real operating conditions.

8. Discussion and Conclusions

1. The rolling resistance coefficient varies according to the condition and type of road surface. Transverse and longitudinal irregularities, ruts and the type of surface all contribute to a higher rolling resistance coefficient. Properly maintained road surfaces reduce this coefficient, extending tire life and reducing the energy required to overcome road resistance, thus reducing fuel consumption.

In surface mining, the rolling resistance coefficient, determined by the type of surface, has a significant impact on energy consumption. However, it can be changed by modifying the road surface. Changing the profile of haul roads is often difficult or impossible due to geological constraints.

Technology roads are a critical part of the open pit infrastructure. Maintaining road surfaces in good condition is costly but has an impact on haulage vehicle fuel consumption, emissions and reliability. Improved road quality not only reduces transport energy consumption and increases vehicle reliability/availability but also significantly reduces mine operating costs. Enhancing haul roads’ quality can reduce energy consumption by approximately 20% per unit mass of fuel consumed, depending on the vehicle type.

2. Analyses of the parameters of unit energy consumption, together with the mass of fuel consumption in various configurations of the operating environment, deepened by the impact of the operating environment on the reliability aspect of the vehicles used, can be successfully carried out for mining plants with a completely different production profile, including underground mines, or in the performance of large-scale earthworks in the construction industry, i.e., wherever road transport is used in non-public and unpaved areas. Furthermore, the parameters used for the analysis in this article can be used to compare vehicles of different types, including electric vehicles (considering the efficiency of the propulsion system, excluding the calorific value of diesel fuel).

3. A significant proportion of vehicle damage is attributed to harsh operating conditions, particularly affecting the power train system, wheels and tires of the Tatra T158, which uses tires designed for construction rather than mining. This not only reduces transport efficiency but also has environmental impacts due to the frequent replacement and disposal of defective tires, increasing the vehicle’s carbon footprint. In light of these factors, while the Tatra T158 is less energy-intensive, the Bell B40E is more reliable under mine conditions, making it a more suitable choice for such environments.

4. The rolling resistance coefficient, which is significantly influenced by the condition and type of technological road surface, plays a dominant role in the energy consumption of surface mining haulage. Proper road maintenance can reduce this coefficient, extend tire life and reduce the energy required to overcome road resistance, thereby reducing fuel consumption. Changing the longitudinal profile of haul roads may be difficult or impossible due to geological conditions, so maintaining good road surface quality remains essential.

5. Four-axle trucks with suitable rims and tires can perform some internal mine transport functions, including periodic transport of ore to first processing points, provided that the transport route is well prepared. Specialized vehicles—articulated trucks—are much better suited to the demanding conditions of an open pit mine and are less susceptible to the effects of the mining environment on their reliability. However, their design features result in higher fuel consumption compared to four-axle vehicles.

6. The actual energy intensity of transport under the specific conditions of the quarry requires further detailed techno-economic analysis to justify the use of haulage. Future work will include modeling and simulation of the impact of the operating environment on transport energy intensity, reliability and technical availability using specialist software, as well as analysis and potential for reducing the carbon footprint of transport modes.