Research on Selection and Matching of Truck-Shovel in Oversized Open-Pit Mines

Abstract

The equipment investment is large and the annual operating cost is high in oversized open-pit mines. The rationality of the truck-shovel selection and matching has a more prominent influence on the economic benefit of mines. Therefore, we set up an optimization method of truck-shovel selection for oversized open-pit mines, and developed a software system of truck-shovel matching to solve the problem of truck-shovel selection and matching for oversized open-pit mines. First, based on the likelihood statistics method to decide the reasonable quantity range of shovels in the oversized open-pit mine, the selection and matching of shovels under different production scales are realized according to the production ability equation of shovels. Then, according to the principal of deciding the truck by shovel, and by deciding the shovel equipment, the truck-shovel efficiency model is constructed, the proper bucket-to-capacity ratio is obtained in the oversized open-pit mine, and the economic range of truck matching under different shovel specifications is determined by the bucket-to-ability ratio. Finally, using this method, the software system of truck-shovel matching is developed by using C# language, which can automatically calculate the quantity of equipment and the material consumption. The results show that the reasonable number of shovels for the oversized open-pit mine should be 4~7, and the bucket-to-capacity ratio should be 4~5:1. When the annual excavation quantity of the mine is 80~150 million tons, the 35~55 m³ shovels should be the mainstream specification. The research results were applied to a new oversized open-pit mine in Tibet, China, and the problem of equipment selection was solved.

1. Introduction

“Broad” and “poor” are the main characteristics of metal minerals in China. With improving mining information and automation, the large-scale mining and large-scale equipment of mineral development become more and more obvious. To adapt to the trend of mineral resources exploitation in China, it is clearly defined that open-pit mines with an ore production scale of more than 15 million tons per year or an excavation (ore and rock) scale of more than 60 million tons per year are classified as oversized open-pit mines according to the Mining Design Code for Metallurgical Mines (GB50830-2013).The single bucket excavator-truck mining technology is still dominant in the oversized open-pit mine. To adapt to the demand of oversized open-pit mines, the world’s large electric bucket capacity can reach more than 55 m³, the truck load capacity can reach 400 t class, and the price of a single piece of equipment is often more than 100 million RMB. In the investment of mine engineering projects, the equipment investment accounts for 50%, and the truck-shovel investment accounts for 80% of the equipment investment. During operation, the total cost of equipment operation and maintenance accounts for about 30~40% of the daily cost of mine production [1]. The pre-stage investment of oversized open-pit mining is huge, and the rationality of the selection and matching of truck-shovel equipment has a more prominent impact on the mine economy.

The traditional methods of mine equipment selection in China are mainly the analogy and experience method, which are selected by comparing the statistical data of shovel matching of existing production mines [2]. With the background of the diversified development of mine equipment, the traditional method has lost its superiority. To choose a more economical equipment combination scheme, a more refined equipment selection and matching method is needed. In recent years, scholars all over the world have tried to adopt various new methods to carry out research on equipment selection and matching. WANG Jia-dong [3] expounded the main basis and principle of the selection of mining and transport equipment based on the actual design of an open-pit mine. Yin Wen-Ying et al. [4] showed an evaluation index system for optimizing open-pit transport equipment from both qualitative and quantitative aspects. Taking the electric locomotive, truck transport and belt conveyor as decision-making objectives, they used the VIKOR (VIse Kriterijumski Optimizacioni Racun) method to optimize the open-pit transport equipment. LIU Shu-Xin et al. [5] combined the traditional open-pit mine equipment selection method with fuzzy hierarchical analysis, and comprehensively evaluated the advantages and disadvantages of equipment combinations through efficiency factors, economic factors and maintenance factors. Wang Hao et al. [6] analyzed equipment selection and equipment matching, and worked out the open-pit mining equipment selection scheme. CHANG Zhi-Guo et al. [7] analyzed the influence of the selection of the two key factors (truck-shovel ratio and bucket-to-capacity ratio) for matching the truck-shovel in an open-pit mine, and concluded that a reasonable truck-shovel ratio can reduce the owed and waiting time, and that a reasonable bucket-to-capacity ratio should comprehensively consider the carriage volume, the rated load of trucks and the properties of loaded materials. XIAO Guo-Qiang [8] comprehensively used a genetic algorithm and a reliability analysis methods to study the three aspects of shovel selection, truck number matching and truck-shovel system configuration in the truck-shovel system. Luo Zhou-Yong [9] proposed loading system numbers in mines, and the amount of excavating equipment was defined as the loading system number.

Research methods in other countries on truck-shovel selection and matching are similar to those in China, mainly including the empirical analogy method, genetic algorithms, the expert system method, the queuing theory method and the linear programming method [10,11,12,13,14,15,16,17]. Scholars all over the world have carried out some research on the equipment selection and matching method of open-pit mines, and obtained some results. However, there are few studies on the truck-shovel selection and matching method of oversized open-pit mines, which are still mainly based on the subjective judgment of experience, and lack of systematic theoretical basis support. Therefore, we construct an optimization method of truck-shovel selection for oversized open-pit mines, and develop the software system of truck-shovel matching to solve the problem of truck-shovel selection and matching for oversized open-pit mines. Firstly, based on the probability statistics method to determine the reasonable quantity range of shovels in the oversized open-pit mine, the selection and matching of shovels under different production scales are realized according to the production capacity equation of shovels. Then, according to the principle of determining the truck by shovel, and on the basis of determining the shovel equipment, the truck-shovel efficiency model is constructed, the appropriate bucket-to-capacity ratio is obtained in the oversized open-pit mine, and the economic range of truck matching under different shovel specifications is determined by the bucket-to-capacity ratio.

2. Selection and Matching of Shovels

Oversized open-pit mines have the characteristics of a large excavation volume per year and a wide working face, and need large equipment to match with it. For oversized open-pit mining, electric shovels are mainly used, and therefore it is more practical to carry out the selection research with electric shovel equipment. The selection of the truck-shovel should first determine the specifications and quantities of shovel equipment, and then configure transport equipment according to the shovel equipment.

2.1. Production Capacity Calculation of Shovels

The production capacity of the electric shovel is related to bucket capacity, single bucket cycle time, full bucket coefficient, ore-rock loose density, material weight, equipment attendance rate, time utilization rate and the annual working system of the mine, etc. The calculation formula of the annual production capacity of the electric shovel is as follows [18]:

$$A_c=\frac{3600M\times k_a\times k_b}{t}\times V_c\times k_m\times k_s$$

(1)

where A_c is the production capacity of the electric shovel (t/(unit •a)); M is the annual working hours of the mine (h); t is the cycle time of a single bucket of an electric shovel (s); $k_a$ is the equipment attendance rate (%), which is the ratio of the equipment available time after equipment maintenance, climate effects and other factors causing an equipment non-working state to the total planned working time; $k_b$ is the equipment time utilization rate (%), referring to the ratio of the pure working time of the shovel to the attendance time of the shovel; V_c is the bucket capacity (m³); k_m is the full bucket coefficient, which is related to the structural form of the bucket, the degree of fragmentation and lumpiness of materials, and the operating level of the driver. k_s is the loose density of materials (t/m³).

The mine production practice shows that the main factors affecting the production capacity of a fixed bucket capacity shovel are the attendance rate and utilization rate of the equipment. The production scale of Toromocho Copper Mine in Peru is 117,000 tons per day, and the annual total migration of materials (including stope excavation volume and stockpile transfer volume) is 100–125 million tons. Two types of electric shovels of 45 m³ and 55 m³ and 38 m³ front-loading machines are mainly used for excavating, and 370 t trucks are equipped to transport materials. Its mining and management level is in the middle level of oversized open-pit mines, which is generally representative of oversized open-pit mines. The actual indicators of the attendance rate and utilization rate of the shovel system equipment from 2015 to 2018 are shown in

Table 1. Attendance rate and utilization rate of truck-shovel equipment in Toromocho Copper Mine.

Item		2015	2016	2017
		Average	Average	January	February	March	April	May	June	July	August	September	October
shovel	attendance rate/%	91.8	89.2	93.1	88.4	91.2	86.6	88.7	76.0	76.0	90.9	85.7	90.8
	utilization rate/%	87.7	78.5	76.1	77.6	80.4	78.1	82.3	84.1	84.1	82.8	81.5	78.5
truck	attendance rate/%	87.2	82.4	79.8	74.3	73.1	70.9	76.8	83.5	83.5	86.6	83.5	86.6
	utilization rate/%	83.0	80.5	77.8	78.0	80.0	81.9	83.0	80.2	80.2	79.3	77.8	74.2
Item		2017		2018
		November	December	January	February	March	April	May	June	July	August	September
shovel	attendance rate/%	88.4	91.3	92.6	82.1	86.0	88.4	85.0	88.4	76.1	91.9	87.8
	utilization rate/%	81.8	82.0	81.6	81.5	81.9	82.1	84.0	85.9	84.6	83.5	83.0
truck	attendance rate/%	81.9	78.6	76.2	72.4	72.0	76.1	76.9	79.9	75.5	75.5	79.4
	utilization rate/%	78.8	79.6	78.1	79.4	79.7	78.1	81.0	82.0	83.1	79.8	77.9

[19].

According to Table 1, the average attendance rate of the shovel was 88.8%, and the utilization rate was 82.4%. The average truck attendance rate was 81.3% and the utilization rate was 80.6%. The results show that the present large electric shovel equipment has a higher attendance rate than the truck, and the failure rate is relatively low.

2.2. Analysis of Reasonable Number of Shovels

The key problem of shovel selection is to determine a reasonable number of shovels to meet the production requirements, so as to select a suitable bucket capacity according to a reasonable number of shovels. It is important to neither choose too many shovels nor too few. The number of shovels selected cannot be too large or too small. If the number of shovels is small, once the equipment fails, it will directly affect the mine capacity, causing economic losses, high risk and low reliability. Moreover, due to the excessive concentration of equipment capacity, it is not conducive to long-term balanced mining in the stope; if the number of shovels is large, the working face of the stope is increased, the management is complex, and the production cost is increased, so it is difficult to realize large-scale and high-intensity mining.

The actual number of pieces of equipment in attendance is a random fluctuation value, and follows the binomial distribution law [20]. Assuming that there are N registered shovels, the average attendance rate of each shovel is p, and the probability of the actual number of attendance stations is:

$$P(X=m)=C_N^m{P}^m{(1-p)}^{N-m} , m=1,2,\dots ,N$$

(2)

where m is the number of equipment units in attendance at the same time, and P (X = m) is the probability of m equipment units in attendance at the same time. $C_N^m$ is the number of combinations when m equipment units in N registered shovels are on duty at the same time.

Table 2. Probability relationship between the number of registered shovels and the number of actual shovels.

Probability		The Number of Registered Shovels
		1	2	3	4	5	6	7	8
the actual number of shovels in attendance	0	0.11	0.0121	0.001331	0.000146	0.000016	0.0000018	0.000000195	0.000000021
	1	0.89	0.1958	0.032307	0.004738	0.000652	0.0000860	0.000011037	0.000001387
	2		0.7921	0.261393	0.057506	0.010543	0.0017396	0.000267894	0.000039291
	3			0.704969	0.310186	0.085301	0.0187663	0.003612508	0.000635801
	4				0.627422	0.345082	0.1138772	0.029228473	0.006430264
	5					0.558406	0.3685479	0.141890951	0.041621346
	6						0.4969813	0.382675594	0.168377261
	7							0.442313349	0.389235747
	8								0.393658881
	≥3			0.704969	0.937608	0.988789	0.9981727	0.999720875	0.9999593

shows the probability of the actual number of shovels in attendance under the condition of a different number of registered shovels. The attendance rate of a single shovel is 89%.

According to the efficiency of existing large excavating equipment and the need for mining and stripping volume, it is generally required that at least three working platforms should be arranged in the stope at the same time to carry out mining and stripping operations [19]. It can be seen from Table 2, considering the reliability index 0.85, in order to ensure that at least 3 shovels are in continuous operation state, at least 4 registered shovels should be selected. When the number of registered shovels is 6, the probability of ensuring the continuous operation of 3 electric shovels is close to 1.0, indicating that the continuous increase of the number of shovels has no obvious effect on the production guarantee of the mine, and it is easy to lead to the situation that the stope has too many requirements and the working face cannot be rationally arranged. At the same time, according to the average attendance rate of 80% of shovels in Chinese mines, the results show that in order to ensure at least 3 shovels in continuous operation, it is more reasonable to choose 5~7 registered pieces of equipment. Therefore, considering the difference of equipment efficiency comprehensively, the reasonable range of shovels in oversized open-pit mines should be 4~7.

2.3. Determination of Shovel’s Bucket Capacity

According to the mine’s annual mining and stripping amount, the required annual production capacity range corresponding to the reasonable number of shovels can be calculated, so as to select the bucket capacity that meets the production scale.

$$V_c=\frac{A_{ky}\times t}{m\times (3600M\times k_a\times k_b)\times k_m\times k_s}$$

(3)

where A_ky is the annual mining and stripping volume (t/a); m is the reasonable number of shovels, taking 4~7; and other symbols have the same meaning as Formula (1).

The study on the selection of shovels is carried out for oversized open-pit mines which require an annual mining and stripping volume of 60 million or more. The average attendance rate of shovels is 88.8%, the utilization rate is 82.4%, the annual working system is 300 days, the full bucket coefficient is 0.85, the single bucket cycle time is 40s, the loose weight is 1.77 t/m³, the unbalanced coefficient of excavation volume is 1.1, and the required bucket capacity configuration under different excavation volume can be calculated, as shown in

Table 3. Bucket capacity configuration for different excavation scales.

Excavation Scales (104 t/a)	Bucket Capacity Configuration under Different Number of Shovels (m3)
	4	5	6	7
6000	26	21	18	15
7000	31	25	20	18
8000	35	28	23	20
9000	39	32	26	23
10,000	44	35	29	25
11,000	48	39	32	28
12,000	53	42	35	30
13,000	57	46	38	33
14,000	61	49	41	35
15,000	66	53	44	38
16,000	70	56	47	40
17,000	74	60	50	43
18,000	79	63	53	45
19,000	83	67	55	48
20,000	88	70	58	50

.

As can be seen from Table 3, the oversized open-pit mine should be equipped with shovels of more than 15 m³. When the annual excavation volume is 80~150 million tons, the shovel with a bucket capacity of 35~55 m³ should be the main configuration specifications. When the total annual excavation volume is more than 150 million tons, the shovel with a bucket capacity of more than 55 m³ can be considered, but at the same time, it should be determined comprehensively in consideration of mining conditions, ore blending flexibility, and the maturity of equipment application. The above table presents the results obtained under certain assumed parameters. Although the parameter selection is representative, the equipment performance of different equipment manufacturers is different, and the material properties of different mines are also different. Therefore, when the conditions change, the results may have a small range of deviation, but the general principle is basically unchanged. The actual mine production all over the world shows that, at present, oversized open-pit mines mainly adopt the bucket capacity specifications of 35 m³, 45 m³ and 55 m³ [21], which have high production efficiencies and low cost, and are suitable for oversized open-pit mines.

3. Selection and Matching of Trucks

3.1. Efficiency Model of Truck-Shovel and Determination of Bucket-To-Capacity Ratio

In the process of open-pit mine design, the general principle of truck-shovel selection is to decide the truck by shovel, which is often represented by the bucket-to-capacity ratio. The bucket-to-capacity ratio refers to the ratio between the volume of truck compartment and the bucket volume of the shovel, and the best bucket-to-capacity ratio is the value when the comprehensive working efficiency of the truck-shovel system reaches the maximum [22,23,24].

The working efficiency of the truck-shovel system is composed of two parts: the truck working efficiency and the shovel working efficiency. The truck working efficiency is the proportion of the transport time in the total cycle time, and the transport time is related to the transport distance of materials, while the total cycle time refers to the sum of the time needed to complete the material transfer, including loading, unloading, turning around and staying, in addition to the running time. The working efficiency of the shovel is the ratio of the effective working time to the total cycle time. The effective working time is the loading time. The total cycle time also includes the waiting time. For a particular mine, after deciding the type of shovel and transport distance, the operation time of the truck is fixed. The larger the bucket-to-capacity ratio, the longer the loading time of the truck, and the lower truck’s working efficiency will be. On the other hand, the larger the bucket-to-capacity ratio, the longer the loading time of the shovel, the higher the shovel’s working efficiency will be. It can be seen that the bucket-to-capacity ratio is negatively correlated with the working efficiency of the truck, and positively correlated with the working efficiency of the shovel. Therefore, to study the best bucket-to-capacity ratio of an oversized open-pit mine, the working efficiency of the truck-shovel should be comprehensively considered. Meanwhile, to reflect the influence of different processes on mine economic benefits, the unit production cost of each process is taken as the efficiency weight, and the efficiency analysis model of the truck-shovel system is established as follows:

$$P=\frac{C_t}{C_{ts}}{P}_t+\frac{C_s}{C_{ts}}{p}_s$$

(4)

where P is the comprehensive efficiency of the truck-shovel system, P_t is the work efficiency of trucks, P_s is the work efficiency of shovels, C_t is the unit cost of materials in the truck transport process (RMB/t), including the cost of the truck depreciation, maintenance, labor wages, fuel consumption, tires and other vulnerable and consumable parts; C_s is the unit cost of materials in the shovel loading process (RMB/t), including the cost of the depreciation, maintenance, labor wages, power consumption, teeth and other vulnerable and consumable parts; C_ts is the unit cost of materials (RMB/t) of the truck-shovel system, equal to C_t + C_s.

The above model can be further transformed into a truck-shovel efficiency model related to the bucket-to-capacity ratio:

$$P=\frac{C_t}{C_{ts}}\left(\frac{t_{ty}}{T_t}\right)+\frac{C_s}{C_{ts}}\left(\frac{R\cdot t_s}{T_s}\right)$$

(5)

where t_ty is the truck transport time (min); T_t is the cycle time required by the truck to complete the transfer of materials (min), including loading, transport, unloading, turning around and staying, etc. R is the bucket-to-capacity ratio; t_s is the loading time of the shovel’s single bucket (min); T_s is the cycle time of the shovel (min), including loading time and waiting time. The meaning of C_t, C_s and C_ts is the same as in Formula (4).

Considering the mainstream shovel types of 35 m³, 45 m³ and 55 m³ in oversized open-pit mines, the relationship curves between bucket-to-capacity ratio and comprehensive efficiency of the truck-shovel under different transport distances (2 km, 3 km, 4 km and 5 km) are established, as shown in Figure 1, Figure 2, Figure 3 and Figure 4.

According to the analysis in Figure 1, Figure 2, Figure 3 and Figure 4, the best bucket-to-capacity ratio is related to the bucket size and transport distance. With the increase of bucket size, the best bucket-to-capacity ratio decreases, while the transport distance increases, the best bucket-to-capacity ratio gradually increases, and the best bucket-to-capacity ratio is mainly distributed in the range of 4–5:1. Therefore, for the oversize open-pit mine, the overall economy is better when the 35 m³, 45 m³ and 55 m³ shovels are used and the bucket-to-capacity ratio is 4–5:1. However, the best bucket-to-capacity ratio should be determined comprehensively according to the truck-shovel efficiency model curve based on the transport distance of the mine.

Accordingly, truck specifications suitable for matching 35 m³, 45 m³ and 55 m³ shovels can be calculated, and the results are shown in

Table 4. Truck matching for different shovel specifications.

Shovel Specifications	Truck Loading Weight	Bucket-To-Capacity Ratio
35 m3	220~270 t	4~5
45 m3	270~330 t	4~5
55 m3	330~400 t	4~5

.

It can be seen from Table 4 that the 35 m³ shovel is suitable for matching 220~270 t trucks, the 45 m³ shovel is suitable for matching 270~330 t trucks and the 55 m³ shovel is suitable for matching 330~400 t trucks. Considering the differences in the load tonnage and unloading height of trucks produced by different equipment manufacturers, it is necessary to further show whether the loading use coefficient of trucks and the unloading height difference meet the needs during the selection of truck-shovel equipment.

3.2. Calculation of Truck Quantity

The number of trucks is related to the annual transport volume, loading weight, transport distance, running speed, time required for a truck turnover, attendance rate and utilization rate.

$$N=\frac{A\times k_3}{G\times k_1\times (60M\times k_a\times k_b/T)}$$

(6)

where N is the number of trucks; A is the annual transport volume (t); $k_3$ is the transport imbalance coefficient, generally 1.05~1.15; G is the rated loading weight of the truck (t); K₁ is the loading utilization coefficient, which generally should not be less than 0.9; M is the number of working hours per year; $k_a$ is the equipment attendance rate, $k_b$ is the time utilization rate; T is the time required for a truck turnover (min), T = t_z + t_y + t_q + t_t, where t_z is the time to fill a truck; t_y indicates the round-trip running time; t_y = 120∗L/V, where L is the transport distance and V is the truck running speed. The horizontal, uphill and downhill running speeds of trucks under empty and heavy loads should be distinguished according to the transport route. t_q is the unloading time, usually about 1 min. t_t refers to turn around and stay time, usually 4~5 min.

4. Software System of Truck-Shovel Matching

Based on the research, the software system of truck-shovel matching is developed by using C# language under NET. SQLite Expert Professional 3.5.78.2498 was used to design and manage the database which contains the main mine equipment specifications on the market, as well as the mine production data of truck-shovel. By inputting the necessary basic indicators such as the work system, the annual mining amount and the physical parameters of material in the project management interface, users can automatically select the equipment and calculate the equipment quantity. Meanwhile, multiple equipment matching schemes can be generated, and the economic comparison of equipment can be automatically completed. It also supports the calculation of material consumption for the recommended device solution and automatically outputs the material consumption report of the device.

The software has the function of memory and storage. The design data generated by the software will be saved in the database for users to query, modify and maintain at any time. It can effectively simplify the work of open-pit mine equipment selection and improve the design efficiency. Figure 5 is the software management interface, Figure 6 is the output interface of equipment selection and matching results, and Figure 7 is the material consumption report of truck-shovel equipment.

5. Conclusions

This paper constructs an optimization method of truck-shovel selection for oversized open-pit mines, and develops the software system of truck-shovel matching. The main results are as follows:

(1)

The reasonable number of shovels for an oversized open-pit mine should be 4~7, and the shovel should be more than 15 m³. When the annual excavation volume is 80~150 million tons, the 35~55 m³ shovels should be the main configuration specifications. When the total annual excavation volume is more than 150 million tons, the 55 m³ shovel can be considered, but at the same time, it should be determined comprehensively in consideration of mining conditions, ore blending flexibility and equipment application maturity.

(2)

Using the truck-shovel efficiency model to determine the optimum bucket-to-capacity ratio is suitable. When the bucket-to-capacity ratio is 4~5:1 in the oversize open-pit mine, the overall economy is better. The 35 m³ shovel is suitable for matching 220~270 t trucks, the 45 m³ shovel is suitable for matching 270~330 t trucks and the 55m³ shovel is suitable for matching 330~400 t trucks.

(3)

The software system of truck-shovel matching is developed by using C# language under the NET framework. Users can automatically select the equipment and calculate the equipment quantity. Meanwhile, multiple equipment matching schemes can be generated, and the economic comparison of equipment can be automatically completed. It also supports the calculation of material consumption for the recommended device solution and automatically outputs the material consumption report of the device.