Improved estimation of open Pit excavator capacity Текст научной статьи по специальности «Строительство и архитектура»

Sergey N. Zharikov

Improved Estimation of Open Pit Excavator Capacity

UDC 622.23

IMPROVED ESTIMATION OF OPEN PIT EXCAVATOR CAPACITY

Sergey N. ZHARIKOV

Institute of Mining, Ural branch of RAS, Yekaterinburg, Russia

The paper addresses issues related to estimation of operational time for open pit excavators during truck loading operations. The author analyzes the method of annual capacity estimation and highlights disagreements in different ways of operational time logging. Recommendations are offered concerning estimation of excavator capacity taking into account its repair cycle. The paper contains an analysis of the cyclical nature of various types of maintenance in the interval between capital repairs as a function of operational time. Guidelines are proposed that allow to calculate annual production days of the excavator with regards to the repair cycle and adjusted utilization coefficient throughout the shift. It has been established that decreasing the coefficient of excavator utilization throughout the shift and more precise logging of annual work days lead to a slower decrease in estimated machine capacity than the one described in the reference literature. According to the suggested method, estimated excavator capacity is more than 23 % higher than the value stated in the reference literature.

Key words: rock excavation, single-bucket excavator, mechanical shovel, mining equipment maintenance, mining equipment capacity

How to cite this article: Zharikov S.N. Improved Estimation of Open Pit Excavator Capacity. Zapiski Gornogo Instituta. 2018. Vol. 229, p. 56-61. DOI: 10.25515/PMI.2018.1.56

Introduction. Currently the operation of open pit shovel loaders is extensively covered in technical scientific literature. However, following existing guidelines, it is still problematic to calculate excavator capacity for specific sites, and different plants with similar conditions may have estimations that deviate from the average value by factor of 2-2.5. The reasons for that are quite diverse - starting from natural and technological ones and ending with organizational difficulties and personnel qualification [10, 11].

Problem Statement. According to [2], actual excavator capacity is always lower than the maximum possible one due to organizational drawbacks that can only be amended by rational use of operational time, whereas works [1, 4] demonstrate how the efficiency of excavation operations increases with their intensification. It means that excavating equipment has a certain margin of capacity increase, which shrinks with the intensification of mining operations. Hence, it is feasible to understand how this operational time reserve relates to the sufficient minimum principle and how efficiently equipment is used in the particular circumstances. The maintenance of mining equipment is associated with significant costs borne by the company, and in case this equipment is used inefficiently such costs become company's losses. This problem is definitely worth considering, and the first aspect that should be paid attention to is the method of capacity estimation, presented in the reference literature [7]. Let us refer to it as the initial method.

Method and discussion of results. According to [7], operational capacity of a single-bucket excavator can be estimated as follows:

= , (1)

t t +1 kf

c op m fr

where E - bucket volume, m3; tc - cycle duration, s; top - time of stationary operation, min; tm -time of equipment movement, min; kb - bucket fill coefficient; kfr -fragmentation coefficient; ku -coefficient of time utilization; Tshft - shift duration, hr.

According to [7], for truck loading operations coefficient of time utilization amounts to 0.8-0.9. This spread serves as a starting point for a whole range of contradictions associated with further estimation of annual excavator capacity.

Shift operation of mining equipment can be examined in closer detail on the example of excavators EKG-8I and EKG-10. Detailed performance characteristics of these mining machines are described in specification documents [2, 3]. Throughout the shift, time allowances are the following: preparatory operations and machine servicing tprep = 31 min; personal time tpers = 10 min; scheduled

breaks and bulldozer waiting time tsched = 10 min; lunch break tlunch = 30 min; truck waiting time -0.4 min; truck maneuver time - 0.5-0.7 min. In general, for each truck the loading time is extended by approximately 1 minute. Assuming that the an EKG-10 loads blasted rock mass into a 130-ton truck, the loading time for one machine is 4.0-5.0 minutes, i.e. this addition amounts to 20 % of the total loading time.

In case of continuous truck feed the loading time equals

tload = Tshift (tprep + t pers + tsched + tlunch + 0'20 tload )

or

t = T — t — t — t — t — 0 201

load shift prep pers sched lunch ' load

t + 021 = T — t — t — t — t

load load shift prep pers sched lunch

from which then

Tshift (tprep ^ pers ^sched tlunch ) tload = 12 ' V2)

For an 8-hour shift the loading time equals 5, 6 hours, for a 12-hour shift it is 8, 9 hours. Hence, the coefficient of time utilization in the former case will not exceed 0.7 and in the latter -0.74. So technologically the coefficient of excavator time utilization for truck loading operations cannot be greater than the above mentioned values. Thus, either the initial method significantly overestimates annual capacity, or overall down time of the equipment make up for the difference, bringing the values to a certain average.

The second ambiguous value in the estimation of excavator capacity is the number of work days per year (on the average, 245 days), which equals 68.5 % of the annual production time. It means that almost a third of the overall time the excavator does not load the rock mass in the trucks. First of all, it can be explained by different types of repairs (preventive overhaul, maintenance, capital and damage repairs). However, repair cycles have different lengths, the interval between some of them is longer than one year, and the logging of repair time which did not take place in the current period eventually leads to mistakes in capacity estimations, so this issue should also be addressed in greater detail.

Below there is an analysis of the duration of repair cycles for excavators EKG-8I and EKG-10 in the light of repair and maintenance data [5, 6, 8, 9]. Statistics from Table 1 served as a basis for establishing a dependence between accumulated repair time of the equipment and machine hours worked between the capital repairs of the mechanical shovel with a 10 m3 bucket (Fig.1). It can be formulated as follows:

t « 0.2 tm, (3)

rep cr ' V /

where trep - time spent on repair and maintenance after the capital repair of the excavator; tm - machine hours worked after the capital repair.

Expression (3) does not take into account time spent on the capital repair itself, but includes the time between two capital repairs in the cycle. Apparently, the time spent on repair and maintenance is directly proportional to the machine hours worked between capital repairs in the full repair cycle. It should be noted that many plants use imported equipment which has a different repair system. In the large companies the number of foreign excavators may exceed the number of EKGs. In the latter case repair systems of domestic machines are usually adjusted to the repair schemes of imported equipment. This way time expenditures are slightly reduced, and the expression (3) may change or have another degree of confidence, but principally the estimation approach stays the same.

Table 1

Duration of repair cycles between capital repairs of the mechanical shovel with a 10 m3 truck [6]

Repair type Time between overhauls, machine hours Repair time, hours Machine hours worked by the excavator between two capital repairs Accumulated repair duration within the repair cycle, hours

I 466 60 466 60

I 466 60 932 120

I 466 60 1398 180

I 466 60 1864 240

I 466 60 2330 300

I 466 60 2796 360

M1 2800 120 2800 480

I 466 60 3266 540

I 466 60 3732 600

I 466 60 4198 660

I 466 60 4664 720

I 466 60 5130 780

I 466 60 5596 840

M2 5600 240 5600 1080

I 466 60 6066 1140

I 466 60 6532 1200

I 466 60 6998 1260

I 466 60 7464 1320

I 466 60 7930 1380

I 466 60 8396 1440

M1 8400 120 8400 1560

I 466 60 8866 1620

I 466 60 9332 1680

I 466 60 9798 1740

I 466 60 10264 1800

I 466 60 10730 1860

I 466 60 11196 1920

M3 11200 336 11200 2256

I 466 60 11666 2316

I 466 60 12132 2376

I 466 60 12598 2436

I 466 60 13064 2496

I 466 60 13530 2556

I 466 60 13996 2616

M1 14000 120 14000 2736

I 466 60 14466 2796

I 466 60 14932 2856

I 466 60 15398 2916

I 466 60 15864 2976

I 466 60 16330 3036

I 466 60 16796 3096

M2 16800 240 16800 3336

I 466 60 17266 3396

I 466 60 17732 3456

I 466 60 18198 3516

I 466 60 18664 3576

I 466 60 19130 3636

I 466 60 19596 3696

M3 19600 336 19600 4032

I 466 60 20066 4092

I 466 60 20532 4152

I 466 60 20998 4212

I 466 60 21464 4272

I 466 60 21930 4332

I 466 60 22396 4392

C 22400 2160 22400 6552

Note. I - maintenance inspection; M1, M2, M3 - maintenance overhaul; C - capital repair.

According to the suggested (second) method, yearly repair time tyrep

can be defined as repair duration according to the worked machine hours minus time spent on repairs in the preceding periods:

й й

О ЗД и

'-ö S h J3ÜJS

1н S3 й

,U

ty

rep

= t -Yt

rep

ip rep

(4)

0 -S <1

7000 6000 5000 4000 3000 2000 1000

y = 0.1976X •

R2 = 0.9501

•••

I*

5000

10000

15000 20000 25000

where t>yep

- repair time in the current year; £ t'rpp - accumulated duration of repairs already performed in preceding

Excavator work between capital repairs, machine hours

Fig. 1. Dependence between accumulated time spent on repair and worked machine hours in the interval between capital repairs of the mechanical shovel with a 10 m3 bucket

periods.

Thus, having planned the amount of machine hours that the equipment

has to work in the current year and knowing its production rates in the preceding periods, one can estimate the repair time with a good degree of precision and to specify the number of work days per year.

If there is a capital repair planned for the current year, then its duration is added to the estimated time before and after capital repairs. This can be expressed as follows:

ty = ty-c + tc + tc-y. rep rep rep rep

(5)

where tyr~ecp - part of yearly repair time before the capital repair; tcrep - time of capital repair; tcr-yp -

part of yearly repair time from the capital repair till the end of the year.

Let us consider the suggested method of operational time estimation. According to the plan, the excavator must work 5 000 machine hours per year, in preceding periods it has worked 10 000 machine hours, which means that accumulated repair time in preceding periods amounts to 2 000 hours. Hence, yearly repair time equals 1 000 hours. In case of a 12-hour shift and a 24-hour operation of the plant the excavator down time will be approximately 42 days. The down time must also include a time reserve for emergencies, damage repair and excavator haul - no longer than 20 days. Then excavator operational time throughout the year amounts to 365 - 62 = 303 days.

Here it should be noted that the haulage time can be significantly reduced if the excavator is connected to a diesel generator, placed on the truck following a parallel course with the excavator. This allows the machine to move at a speed higher than 1 km/h, saving up time and increasing effective haulage area.

Attention also should be paid to the estimation of operating excavator park, which depends on the coefficient of excavator reserve

Т

Ks = -L,

n,,

(6)

where Ty - yearly number of work days for the plant; ny - yearly number of work days for the excavator.

Let us assume that the nominal excavator park consists of 10 machines. According to the first method kres = 365/245 « 1.49; according to the second one - 365/303 « 1.20. In the former case operational excavator park equals 0/1.49 « 6.71 « 7 machines; in the latter -10/1.20 « 8.33 « 9 machines. Therefore, even under maximum excavator capacity and high intensity of mining operations underestimation of annual work days results in lower values of the operational excavator park. It can lead to incorrect planning of resources of the repair service as the mining conditions may suddenly demand the introduction of additional machine units.

0

Table 2

Comparison of the production kuny for two methods of excavator capacity estimation under decreasing ku

First method Second method

ku ny kuny ku ny kuny

0.90 245 220.5 0.74 303 224.2

0.88 245 215.6 0.73 303 220.1

0.86 245 210.7 0.71 303 216.0

0.84 245 205.8 0.70 303 211.9

0.82 245 200.9 0.69 303 207.7

0.80 245 196.0 0.67 303 203.6

0.78 245 191.1 0.66 303 199.5

0.76 245 186.2 0.64 303 195.4

0.74 245 181.3 0.63 303 191.3

0.72 245 176.4 0.62 303 187.1

0.70 245 171.5 0.60 303 183.0

0.68 245 166.6 0.59 303 178.9

0.66 245 161.7 0.58 303 174.8

0.64 245 156.8 0.56 303 170.6

0.62 245 151.9 0.55 303 166.5

0.60 245 147.0 0.54 303 162.4

0.58 245 142.1 0.52 303 158.3

0.56 245 137.2 0.51 303 154.2

0.54 245 132.3 0.50 303 150.0

0.52 245 127.4 0.48 303 145.9

0.50 245 122.5 0.47 303 141.8

0.48 245 117.6 0.45 303 137.7

0.46 245 112.7 0.44 303 133.6

0.44 245 107.8 0.43 303 129.4

0.42 245 102.9 0.41 303 125.3

0.40 245 98.0 0.40 303 121.2

Two methods need be compared because the first one handles higher coefficient of time utilization and lower number of work days throughout the year, whereas in the second one there is an opposite situation - lower coefficient and greater number of work days. The most convenient way to carry out the comparison is the following. Let us assume that Qe part of the expression (1) is constant:

3600 E tm

t„

t +1 kf

op m fr

— T shift = COnSt

(7)

Then capacity variation can be explained by changes in the production of coefficient of time utilization and the number of work days throughout the year (kuny). For maximum ku values results for both methods are the following: according to the first one,

kuny = 0.9-245 = 220.50; to the second one - kuny = 0.74303 = 224.22.

The difference is only 1.7 %, which does not exceed permissible estimation error. However, those are results for the maximum ku, which makes them more of ideal values. That is why the dynamics of kuny production should be analyzed under ku value decreasing till 0.4. Corresponding results are presented in Table 2 and Fig.2. Intermediate values have been obtained by means of interpolation, the total of 26 values has been selected.

Jl

O Ö o

o Ph

250.0

200.0

150.0

100.0

50.0

1 3 5 7 9

11 13 15 Value

17 19 21 23 25

1s method

2nd method

Fig.2. Graphs of k„nv variations calculated for both methods under k„ value decreasing till 0.4

According to the graphs, the first method demonstrates greater decrease of estimated capacity from the initial value (55.56 %), as compared to the second method (45.95 %). The difference in capacity under ku = 0.4 is 23.67 % from result of the first method.

Conclusion. The analysis of capacity estimation of a single-bucket excavator demonstrates that for truck loading operations the coefficient of time utilization is lower than the values described in reference literature. Maximum values of ku < 0.74.

A method has been suggested on how to calculate the annual number of excavator work days taking into account repair cycle and adjusted coefficient of excavator utilization throughout the shift.

It has been demonstrated that repair time logging within the full repair cycle results in higher values of work days in the estimation of equipment capacity, which can lead to greater annual capacity of the excavator provided the relevant organization of mining operations. This is possible if the mining operations reach a certain level of intensity, as constant loading of the rock mass should be supported by a relatively high rate of preparatory operations.

Comparing the coefficients of excavator reserve based on the number of work days calculated with two described methods, the author demonstrates how underestimation of annual work days results in a lower number of operational excavator park.

It has been established that decreasing the coefficient of excavator utilization throughout the shift and more precise logging of annual work days leads to a slower decrease in estimated machine capacity than the one described in the reference literature. According to the suggested method, under ku = 0.4 estimated excavator capacity is more than 23 % higher than the value stated in the reference literature.

REFERENCES

1. Gryaznov M.V., Kolobanov S.V. Capacity Resources of Excavating and Transportation Equipment. Mir transporta. 2013. N 3, p. 96-102 (in Russian).

2. Unified Standards of Performance and Time for Rock Mass Excavation and Truck Transportation at Non-Ferrous Metallurgy Plants. Mintsvetmet SSSR. Moscow, 1986, p. 92 (in Russian).

3. Unified Standards of Performance for Open Pit Operations at Mining Plants. Part IV. Rock Mass Excavation and Truck Transportation. Tsentral'noe byuro normativov po trudu. Moscow, 1989, p. 81 (in Russian).

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6. Mel'nikov N.V. Quick Reference Book on Open Pit Mining Operations. Moscow: Nedra, 1968, p. 308 (in Russian).

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8. Decree of USSR State Committee for Labour, All-Union Central Soviet of Trade Unions Secretariat from 23.07.1985 No. 235/16-21. On the Adoption of Time Standards for the Repair of Mechanical Shovel Excavators EKG-8, EKG-8I. URL: http://bestpravo.ru (date of access 19.02.2016).

9. Operational Guidelines 3536.00.00.000RE Open Pit Caterpillar Excavators EKG-10, EKG-8US and EKG-5U. OAO «Izhor-skie zavody». St. Petersburg, 2000. URL: https://www.twirpx.com/file/615105// (date of access 23.01.2017).

10. Baskakov V.P., Borzykh E.V., Zhivotyagin A.M. et al. Standardization of Production Operations as a Key Trend of Plant and Company Development. Moscow: Gornaya kniga, 2010, p. 46 (in Russian). (Seriya «Biblioteka gornogo inzhenera rukovodite-lya». Iss. 4).

11. Khokhryakov V.S. Open Pit Development of Mining Fields. 5-e izd., pererab. i dop. Moscow: Nedra, 1991, p. 336 (in Russian).

Author Sergey N. Zharikov, Candidate of Engineering Sciences, Senior Researcher, 333vista@mail.ru (Institute of Mining, Ural branch of RAS, Yekaterinburg, Russia).

The paper was accepted for publication on 7 July, 2017.