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Controlling Efficiency and Production Goals in an Underground Mine

VICTOR O. TENORIO*1 and SUKUMAR BANDOPADHYAY2

In a trackless underground mine, the process of extracting ore and transport the materials to the next stages of

production involves using high-mobility LHD (load-haul-dump) vehicles. Transportation activities need to be

synchronized with other activities to achieve production goals in an efficient, safe and economical way. What

may seem apparently simple, requires the adjustment of several parameters to take advantage of the available

equipment, optimize equipment schedule and operation without interruptions or unwanted delay times. This

paper presents an application of GPSS/H simulation software for an underground copper/zinc mine, in which

ore production is expressed as run-of-mine grades. The production tonnages for copper ore controlled on a

daily basis, includes scoop breakdowns, which impact in performance, and helps to combine the equipment

in a more efficient way without additional investment. Results of simulation experiments for a 30-day period

proved to be useful in producing a stable value for ore grade.

Keywords: underground; simulation; GPSS/H; efficiency; control; production

1. Introduction

Mineral production in an underground mine involves a number of unit operations for extracting

ore from stopes, typically in cyclic schedules. Considering the high investment in equipment and

the cost of labors, there is a need for efficient and economically viable operation despite all the

constraints imposed on the operation in terms of space, number of load-haul-dump units and the

need to achieve production goals in restricted periods of time is paramount.

For the simulation study presented here, a simplified model for an underground

copper/zinc mine in operation has been developed, in which variations in the grade of the run-of-

mine ore were observed, as well as considerable underachievement of production goals occurred,

1 Corresponding author: Victor O. Tenorio, Graduate Research Assistant, College of Engineering and Mines, University of

Alaska Fairbanks, P.O. Box 2155 AK 99775-2155, USA, Phone: (907) 455-3069, Email: ftvot@uaf.edu

2 Professor, College of Engineering and Mines, University of Alaska Fairbanks

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originally scheduled as 3,000 tpd (2,000 tons of sulphides and 1,000 tons of oxides). Solutions

for reaching these production goals are proposed with the minimum investment possible,

pertaining to LHD equipment increase, variation of the speed of the unit-train convoy, or

modification of the labors requirements.

The approach presented for identifying the problem and to seek for a practical solution is

through system simulation experiments. According to McIntosh (1999), “[Computer] simulation

is a powerful tool that can be used to reduce underground mine development and operating

costs.” It can also be used to minimize risk when planning investments (Ritter, 1999). Successful

results have been obtained in works presented by Sturgul and Smith (ca. 2000), Brunner, Yazici

and Baiden (1999), Lebedev and Staples (2000), in simulations for a coal mine (Schafrik, 2001),

and in challenging projects such as the transition from an open pit to an underground mining at

Palabora (2002).

2. The Ore Deposit

The model presented here is based on a hypothetical volcanogenic massive sulphide deposit

(Fig.1), with a varying degree of structural and mineralogical complexity that includes the

alteration by weathering, giving rise to a secondary enrichment in copper oxide.

The geometrical characteristics of the deposit have been simplified, and the adjacent zinc

orebody associated with the same mineralogy, and the waste from the surrounding host rock,

have been overridden in order to focus the problem in the copper-sulphide and copper oxide ore

extraction.

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Figure 1.- 3D Model of a copper-zinc orebody

3. The Mine Layout

The mining method selected for this mine is ascending cut-and-fill, which will allow to follow

the upward massive bodies, along with chambers with grids opened in the surrounding host rock,

defined as “dumping facilities” (Fig. 2). There is a bin for storage of each type of the ore. Both

bins have ore chutes with pneumatic gates to allow discharge to mine cars from a unit-train

convoy in the main level of mine development openings. Beyond this point, the ore is

transported to the stock piles at the surface, using the vertical hoist (Fig. 3). The ore bins have

been designed to allow increase their dimensions in future expansions of the project. Also as the

current stopes become depleted, additional ore chutes will be opened and the current dumping

facilities can be moved to superior levels, following the mineralization.

Other possible expansion options include the opening of an additional extraction tunnel

and/or changing the size of the current mine cars, which will also be considered for the current

exercise.

pp. 1233-1248

4. The Production Process

Underground infrastructure for extracting the ore consists of a main shaft, which is equipped

with skips for transporting the ore from an ore bin at the bottom (sunken bin), to the surface,

where it goes to separate stockpiles, one for the sulphide ore, and the other for the oxides. The

sulphide ore will be treated at the concentration plant in a crushing-milling-flotation process, and

the oxide ore will be transported to a leaching pad.

Neither the concentrator plant/leach pad nor the shaft reported any breakdowns. They

will not be included in the model. Thus, focusing on the operations mainly in the mining and

transportation of the ore from the stope, until it is delivered by the car-convoy to the sunken bin.

This means that the production quantities will be registered when the mine cars unload at the

bottom of the shaft.

5. Stopes

In the model presented, there are two stopes for the sulphide ore and two for the oxide ore. Each

of the sulphide stopes (A and B) is served by 6 cu yd scooptrams, while the oxide stopes (C and

D) are served by 3.5 cu yd scoops, respectively (Fig. 4).

Scoops 2B and 4D are new, whereas the scoops 1A and 3C are old, and several

breakdowns have been recorded during the day. For a simplified representation of these

breakdowns, the status check for the scoops is made immediately after the ore dumping.

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Figure 2.- Generic layout of the underground mine for the study

(not in scale)

Figure 3.- Shaft with ore skip

For every stope, an access ramp allows the scoops to load and haul their loads in the

indicated times. The destination is the “dumping facility”, which will be used exclusively for the

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scoop that arrives first, forces the other scoop to hold until the facility is completely released.

Additionally, there is a warning light that indicates that the bin is full and no additional dumping

is allowed. This is to prevent overfilling the ore chutes and to avoid any operational problem

during the ore discharge process. Although these actions are convenient for safety purposes,

there is, however, a concern if the scoops are left to idle for considerable periods of time.

6. Ore Bins

The layout has two ore bins, as shown in Fig. 5, one for the sulphide, which is identified as Bin-

S, and has a capacity of 1,500 tons, which in terms of scoop dumps will be equivalent to 109 (of

6 cu yd) units, and the other is for the oxide, named Bin-O, and with a capacity of 500 ton or its

equivalence in 62 (of 3.5 cu yd) units.

When the bins reach their maximum capacity, the corresponding dumping facility is

closed, and the scoop operators have to hold their loads near the designated ore chute.

This procedure, in addition to being highly inefficient for the continuity of the

production, may cause a considerable variation in the ore grade, decrease the daily tonnage,

resulting in a costly underutilization of the scoops.

7. The Convoy

Right over the extraction tunnel, there is a chute for each one of the bins for discharging the ore

in the mining cars pulled by a locomotive. The convoy of cars arrives approximately every 4

hours to perform the discharge. A higher priority is given to sulphides ore cars in order to feed

the plant properly, whereas the oxides ore cars go to a leaching pad, and thus require less strict

time frames.

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Figure 4.- Stopes A-B (sulphide) and C-D (oxide)

The total of mining cars in the convoy can carry up to 500 tons of either type of ores.

However, the discharge is made only of one ore type at a time, interrupting the ore dumping

process while the mine cars are loaded.

Despite the fact that all scoops have their own defined routes from the stopes to the

dumping facility, it is evident that with the present layout the ore production requirements are not

fulfilled. A study of the current operation of the system for a period of at least 30 days is

required.

The production management wants to consider the effect of duplicating the number of

scoops in each stope, or increasing the interarrival time for the convoy when sulphides are

required to be discharged (Fig. 6).

8. Design, Analysis and Programming

A particular version of General Purpose Simulation Software (GPSS/H), has proved to be useful

for simulation in complex underground mining operations, as demonstrated by Sturgul and Smith

(ca. 2000), and with valuable results which provided significant solutions for bottlenecks (Ritter,

1999), or the reduction of costs (McIntosh, 1999).

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Figure 5.- Bins for Sulphide and Oxide, respectively

Figure 6.- Arrival time for convoy and bin discharge times

Using the mine layout shown in Figures 4, 5, and 6, the proposed block diagrams reflect

the logic sequences of operation of the whole system (Fig. 7), including the estimated times for

the scoops and train arrival to perform their activities.

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Gates in the logic diagram are included to prevent the scoops to dump while the train is in

the process of discharging the ore from the chutes.

Each of the GPSS/H instructions is depicted with the corresponding symbol and in cases

where times and other parameters need to be represented, they are clearly noted (Fig. 8).

Figure 7.- Block Diagram for the Simulation

Fig. 8. Partial view of the GPSS/H programming code

For the breakdowns, different for scoop 1A and scoop 3C, a split random function

represents the proportional percentage in which the scoops need to be repaired, and a normal

distribution model describes the statistics of repair time (Fig. 9).

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An instruction for defining storage allowed to represent the ore bins and their capacities,

with indicators of whether they are full or not. Additional instructions will indicate to increase

the units (ore dumps) for the scoops, or decrease the amounts when they correspond to the ore

dumping to the mine cars of the convoy.

Figure 9.- Normal Continuous Distribution

Source: Schafrik (2001)

9. Initial Parameters

At the beginning of the process, the following assumptions are made:

o There is no reference to the status of the system at the end of a previous 30-day period

(no accumulated initial values).

o Bins are empty.

o All scoops are in the corresponding face of their assigned stopes, ready to load ore.

o The convoy comes from its point of start, and mine cars are empty.

o There are no bottlenecks at the ore passes.

o There are neither lunch breaks, nor blast schedules (or they occur without producing any

interference in the activity being studied).

o There is no count on waste removal (omitted for the current simulation).

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o There is no consideration for the extraction of zinc in the current model.

o There are no limitations in the ore dumping from the mine cars to the shaft, or from the

shaft to the surface.

o Also there are no limitations in the stockpiles at the surface.

10. Program Execution

Using this input data, a simulation experiment was conducted first for one shift, then for two

shifts (one day), and finally an iteration of the process was made for 30 days (an average month).

The result report is shown in Fig. 10. It is evident that a stand-by unit would considerably help to

maintain and even to increase the production levels, as well as maintain a convenient level of

productivity. However, part of the management goals is to optimize the current system without

any additional investment in equipment.

The program was executed smoothly, with the corresponding instructions to generate the

output report on a separate file. Several alternative scenarios were examined, such as the number

of scoops or the occurrence of breakdowns. The internal report of the simulation that contains the

intermediate status of the variables throughout the process can also be helpful for studying the

way some of the values, in particular the storage, flow during the analysis time.

An important result obtained from the analysis is that the grade variation achieved in the

blending process is minimal, which means that the current design, although less efficient in the

use of the available equipment and in the synchronization of the convoy, accomplishes the

objective of keeping a uniform and proportional combination of ore from each of the stopes (Fig.

11 and Fig. 12).

pp. 1233-1248

Figure 10.- Partial Output from Report

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Tonnage Output of Cu during a 30-day simulation

0200400600800

10001200

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Days

Tons

Suplhides Oxides

Fig. 11.- Tonnage Output

Variation of Cu grade during a 30-day simulation

0

2

4

6

8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Days

%

Sulphides Oxides

Fig. 12. Variation of Copper grade

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11. Conclusions

The production model of an underground mine system was a successful demonstration of the

power and versatility of GPSS/H as an analysis tool. With few code lines, the language can

describe a real-life situation, examine the impacts of changing parameters and operation modes

and obtain results for different scenarios. The two main objectives in the present project can be

summarized as follows:

• Analyze current underground mining layouts and look for a better way of working with

the available equipment.

• to propose the use of simulation language, now available for the standard computer

technology

These two objectives are in fact “symbiotic” (Schafrik, 2001), and should reflect the way

future mining operations must work, always looking for optimal results in production.

The experimental model used shows the results of a set of 30 continuous days and how

the scoops have to deal with queues, and with the synchronized access to the ore bins. The data

corresponds to a particular set of conditions, which include the performance of the equipment

(loaded and unloaded), a defined timetable for the convoy all this and without taking into

consideration waste removal, lunchtime, blasting schedule, etc. The interarrival times and the

breakdowns were handled using normally distributed random numbers.

The output report gives a clear view of how the number of breakdowns impacts the

production and how they are a factor to consider when management will decide to increase the

production goals. However, the data is reasonable enough to evaluate the performance of a given

operation (Schafrik, 2001). In conclusion, the data can be used to perform effective “what-if”

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analyses, and as an example, an extended analysis for 12 months will definitely help a better

planning and if necessary a redesign of the current production layout.

12. References

Brunner, D., Yazici, H.J. and Baiden, G.R., 1999, Simulating development in an underground rock mine, SME papers, http://www.systemflow.com/sme99paper.pdf (Accessed December 2004).

Lebedev, A. and Staples, P., 2000, Application of simulation to selection of ore haulage system, http://www.ckit.co.za/Right%20Index/Tech%20Focus/ore%20haulage/Ore%20Haulage.html (Accessed December 2004). Lynx Geosystems, Inc., 2001, Lynx case study (mining): Evaluation and mine planning for an underground mineral deposit, http://www.lynxgeo.com/HTML/Applications/apps_1.htm (Accessed December 2004). McIntosh, S.L., 1999, How computer simulation reduces costs in the underground mining industry. McIntosh Engineering. http://www.mcintoshengineering.com/Services/newservices/simulationpaper.htm (Accessed December 2004). Palabora, 2002, Underground Mining Project, http://www.mine-automation.com/palabora/palabora.html (Accessed December 2004). Pritsker, A.A.B. and O’Reilly, J.J., 1999, Simulation with Visual SLAM and AweSim, John Wiley &Sons, Inc., New York. Ritter, F., 1999, Mining simulation: Multipurpose models for large-scale systems, “Otto von Guericke” University Magdeburg, http://isgwww.cs.uni-magdeburg.de/~fritter/postscript/report.pdf (Accessed December 2004). Schafrik, S.J., 2001, A new style of simulation model for mine systems, Blacksburg, Virginia, http://scholar.lib.vt.edu/theses/available/etd-10042001-101234/unrestricted/thesis.pdf (Accessed December 2004). Sturgul, J.R., 2000, Mine design – Examples using simulation, SME, Littleton, CO. Sturgul, J.R. and Smith, M.L., ca. 2000, Using GPSS/H to simulate complex underground mining operations.