The impact of mixed fleet hauling on J mining operations ... · PDF filetaken from Talpac...

Journal

Paper

Introduction

Mine background

Venetia Mine is situated 80 km west of Musinain the Limpopo Province of South Africa. Twoout of the 13 kimberlite pipes in the area arecurrently being mined using a split shellmining method. This is a modified open pitmethod, chosen since it can optimize themine’s net present value. The final pit limitsfor traditional and split shell open pit methodsdo not differ very dramatically; but thefinances used for waste stripping are deferredto a later stage in the life of the mine. As withany method there are both advantages anddisadvantages, but this paper will focus on thedisadvantages as they affect the productivityof the haul truck fleet to a large degree. Thedisadvantages include:

➤ Truck cycle times are increased due tothe presence of switchbacks in the pit, ascan be seen in Figure 1, specifically thenorth ramp

➤ The turning radii of all the truckspresent on the mine must be consideredin the design of switchbacks to preventbottlenecks from occurring. Whilst along-term plan can accommodate thesmaller truck sizes, as soon as truck sizeis increased, road geometric designshould be revised to accommodate thesevehicles. On a pre-existing roadnetwork, however, this revision is oftenproblematic, the more so within a split-shell mining environment. Productiontime will be lost when trucks have tostop at a hairpin bend turn due to:

– being unable to spot other trucksand/or

– having to wait for oncoming trucksas two trucks will not be able to passon the same section of road

➤ At intersections where ramps meet,trucks will stop during their cycle,causing an increase in cycle time (ascompared with non-interrupted cycles)(Kear and Gallagher, 2002).

Project background

Venetia Mine currently has a mixed haul truckfleet. This is because different haul truckswere chosen at different stages during the lifeof the mine to minimize total transportationcosts for the specific tonnage profiles over agiven time period. The current pit layout andsize do not allow for the purchase ofadditional, similar smaller trucks due to thespace limitation, thus the decision was takento buy fewer, larger trucks i.e. – the CAT793Ds, to deal with the mine’s future tonnageprofile. The haul truck fleet at the mineconsists of the following Caterpillar trucks(Table I):

The impact of mixed fleet hauling onmining operations at Venetia mineby J. Krzyzanowska*Paper written on project work carried out in partial fulfilment of Bachelor of

Engineering-Mining

Synopsis

Venetia Mine, an open-pit diamond mining operation in theLimpopo Province of South Africa, currently has a mixed haul truckfleet consisting of Caterpillar 785B and C; 789C; modified 793D and793D. Even in an ideal situation these trucks have different cycletimes, which causes queuing at the loading area and their differentspeeds cause bunching on the ramps, leading to higher overall cycletimes and lower productivity. This problem was identified but theroot causes of the problem were not investigated and quantified. Atime and motion study on the haul trucks was thus undertaken tomeasure actual cycle times and compare them to ideal cycle times aswell as to observe any reasons for deviations. Ideal cycle times arepartly evaluated on the basis of simulation and partly fromproduction optimization expert input.

Several areas that affect production were identified and theseinclude: haul road conditions, the control room, dispatchingprogramme and dispatching data management, as well as truck-shovel matching. The investigation was important in establishingcontrol parameters for haul fleet operation since time spent queuingis production time lost, which defers waste tonnes to later in the lifeof mine, thus decreasing the tempo at which kimberlite is exposed.

* University of Pretoria, Pretoria, South Africa.© The Southern African Institute of Mining and

Metallurgy, 2007. SA ISSN 0038–223X/3.00 +0.00. Paper received Feb. 2007; revised paperreceived Apr. 2007.

215The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 107 NON-REFEREED PAPER APRIL 2007 ▲

The impact of mixed fleet hauling on mining operations at Venetia Mine

Due to the worldwide tyre shortage the mine does nothave adequate tyre supplies for the 793Ds. A decision hasbeen made to use tyres with a smaller TKPH (tonneskilometre per hour) rating thus decreasing the truck’spayload capability by 45 tonnes. This is the basis of the 793D(modified) in Table I.

All these truck models have varying cycle times, whichwill cause queuing even in an ideal situation. Figure 2 showshow the speed on ramp would vary for each truck type atvarious ramp grades. With a mixed fleet, it is inevitable thatthe maximum speed on ramp will be dictated by the slowesttruck on the ramp, more so when traffic volumes are high.This will lead to bunching on ramps and reduced produc-tivity.

Aim and scope of paper

The aim of this paper is to determine and quantify the mainfactors that affect the productivity of the haul truck fleet atVenetia Mine, especially focusing on the reasons behind thelarge amount of bunching and queuing time, and the role ofmixed fleet operations on fleet productivity.

Current state of the art

Factors affecting haul truck fleet productivity

Time and motion study

The best approach to encompass the factors that influence

bunching on the ramps, queuing at loaders and other factorsthat affect haul truck productivity would primarily be toperform time and motion studies on the haul trucks. This willallow for the collection of actual cycle data. Once the actualcycle data have been collected they can be compared to anideal cycle situation. Time studies would be performed duringday and night shifts to test for any differences. While somedata is available on the current operating practice fromvarious sources, the detail behind the data is insufficient todeduce any definitive conclusions on the reasons for the poorperformance.

Haul road conditions

Haul road conditions (including loading and dumping areas)must be checked to determine whether they are acceptableand ideally suited to the operation. i.e. the geometric,structural and functional designs are to standard, togetherwith the correct operating practices. This will be done bothvisually while performing the time and motion study and alsousing Caterpillar’s Vital Information Management System(VIMS). This is truck on-board software that collects data andanalyses it to give an ASA (application severity analysis)rating, which shows the mine whether the road conditions onsite are acceptable or not, in terms of universallybenchmarked minimum and maximum ratings for varioustruck-/road interaction parameters.

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216 APRIL 2007 VOLUME 107 NON-REFEREED PAPER The Journal of The Southern African Institute of Mining and Metallurgy

Figure 1—Plan view of pit layout (January 2006)

Table I

Haul truck fleet at Venetia Mine

Quantity Payload (t)

785 B & C 9 136789 C 13 180793 D (modified) 1 175793 D 3 220

Figure 2—Ideal simulation of truck speeds for three different Caterpillartrucks

Haul road grade +2% RR

Comparison of truck speeds at various grades

Sp

eed

(km

/h)

DESIGN

ACTUALTDE

CREST

Critical to all hauling operations is a safe minimum roadwidth. Table II sets out some recommended widths, whichformed the basis for an assessment at Venetia. This is criticalsince trucks need adequate space to allow for passing and bi-directional travel on the ramps, otherwise trucks will have towait for one another to pass (either in opposite directions, orwhere a faster truck encounters a slower truck on ramp),thus increasing cycle times.

The minimum turning radii of the trucks must be takeninto account in order for the trucks to have enough room tonegotiate bends in a single turn—this is especially importantdue to the presence of sharp switchbacks in the split shellmining method. It would be ideal if switchbacks wereconstructed wide enough for two trucks to pass one anotherwithout having to stop, but this (as do wider ramp roads) hassignificant impact on mine planning and stripping ratios.

Not only is correct primary road construction importantbut the continual maintenance of roadways is critical. It hasbeen said that preventive measures are better than correctiveones, as it can take up to five times longer to repair a roadthat is badly deteriorated than to continually maintain it(Smart, 2002). In the case of corrective measures, theroadway (or at least part of it) is likely to be closed, whichwill decrease production rates and restrict traffic flow. Tyredamage occurs frequently on mines due to the cutting ofrubber by rocks; hence spillage should be controlled toprevent tyre damage. Spillage itself is often an indicator ofsome other road design deficiency—excessively sharp curves,no super-elevation, grade breaks on ramps, etc., all leading topitching, yaw or rolling of the truck with the attendantspillage.

The causes of deterioration of haul roads include mainlypoor design and construction standards, which becomeevident as:

➤ Wheel rutting (deep tracks which are made by tyres) ➤ Overloading where wheel loads applied exceed that of

the road design value➤ Poor geometric design—grade breaks in ramps, sharp

curves and spillage➤ Water damage that washes away surface layers of the

road ➤ Loose unbound material and dust due to poor surface

on road.

Additionally, deterioration can result from:

➤ Movement of tracked machines destroying the upperlayer of road

➤ Poor skills of operators.

However, while the main haul road network forms thebackbone of the hauling operations, as much (if not more)attention needs to be given to the design and maintenance of

all loading areas. This is to allow for maximum and constantacceleration and a fast pull-away time of the truck, to enableit to enter the ramp at high speed, therefore helping toincrease average speed on ramp. The factors that will permitthis to happen most effectively are:

➤ The loading area must be level and free of anypotholes, preferably dressed with crushed material

➤ There should not be any spillage from previous trucksbeing loaded and any rocks that may have fallen fromthe face

➤ Have no water present as this prevents the driver fromseeing protruding objects and also allows tyres to bemore easily cut

➤ Large enough area so that tight turns are not necessaryas this causes damage to the truck tyres—especially onfull-lock laden maneuvering.

The most important fact to remember is that a haul roadstarts at the loading area and ends at the dump. Maintaininghaul roads is done at the expense of time and money but willkeep production running optimally and control rollingresistance to acceptable levels. It will allow the mine to getclose to optimal cycle times from the fleet and criticallyextended tyre life, which will increase truck availability anddecrease capital cost. This must be taken into account withthe world wide tyre shortage—if a tyre is damaged will it beable to be replaced, or will the whole truck be out ofproduction? Good haul road conditions will increase thenumber of cycles a truck can do in a shift, which increasesproductivity. All these efforts will ultimately decrease therand per tonne cost.

Simulating ideal operating situation

Two different sources were used to gather data that are usedin combination to compile ideal cycles for the various haultruck routes. The hauling and travelling empty times weretaken from Talpac (Runge Mining 2006) simulations.Spotting, loading and pull away times were used assuggested by a production optimization expert that wasavailable at Venetia Mine. An analysis was undertaken toensure that the unimpeded travel times simulated on Talpacwere in fact realistic for the truck types and age used atVenetia. In this way, the reliability of the simulation could beused to realistically assess the time study results and, morecritically, the deviations in the time study data compared toideal simulated operations.

Control room

Venetia Mine dispatches all haul trucks via a centralizedcontrol room. The control room has to be considered in thisinvestigation to asses whether the problem of queuing andbunching that is seen in the pit is solely due to the differenttruck models or also stems from incorrect truck dispatch ornot using the dispatching program optimally.

Truck-—shovel matching

The final consideration that directly affects queuing at theloaders and in turn further in the cycle is truck and shovelmatching. An increased number of loader bucket passes willlead to increased queuing time for the trucks already waitingat the loader. The ground conditions at Venetia were used to

The impact of mixed fleet hauling on mining operations at Venetia MineJournal

Paper

217The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 107 NON-REFEREED PAPER APRIL 2007 ▲

Table II

Safe design width of haul roads (Smart, 2006)

CAT truck Width of Two-way Two-way truck (m) straight (m) corner (m)

785C 6.64 23.24 26.56789C ; 793C 7.67 26.85 30.68


simulate a correct and realistic number of passes per truck-shovel combination used on the mine. The results aresummarized in Table III. The mine has three different shovelconfigurations manufactured by O&K.

➤ Shovel 306; RH 200; 26 m3 bucket➤ Shovel 307; RH 200; 26 m3 bucket ➤ Shovel 308; RH 340; 28 m3 bucket➤ Shovel 309; RH 340; 34 m3 bucket.

It is important to note that these values depend stronglyon the fragmentation that will occur after each blast and onindividual operator skill.

Hydraulic front shovels should have load cycle times of26–28 seconds per pass with 4–6 passes (Caterpillar, 2002).The values that were simulated fall within this region. It mustalso be noted that with correct blasting and loadingtechniques it is possible to load well- matched trucks in 4passes (4 passes @ 30 s each gives 2 min loading time)(MacLeod, 2005).

It is important to note that only 785s are able to tip oreinto the current crusher due to the crusher’s size limit. Thismeans that, as far as possible, only 785s will be used to loadore by RH 200s (most often) or the CAT 994 front end loader.

Review of current truck fleet operational practice

Data gathering methodology


Time studies were performed on randomly chosen haul trucksduring day and night shifts. Times were taken for each partof the production cycle:

1. Spotting at loading area2. Loading3. Pull-away time4. Hauling (traveling laden)5. Travelling empty6. Queuing

The time taken to dump each load was not recorded.Spotting times were taken from when the truck had finishedmanoeuvering into a position, where it was ready to reverse,till when it was correctly positioned for loading. Anyabnormalities in the cycle or reasons for its extension werenoted.

Ideal hauling and empty travelling times were simulatedon Talpac. Loading and spotting times were not considereddirectly from Talpac but were sourced (MacLeod, 2005). Thiswas done in order to get the most ideal cycle time that themine could aspire to. In terms of experiments, it would be the

control that results are compared to. Queuing is a normal partof mining operations, but should be kept to a minimum toincrease production time.

Haul road conditions

Conditions on the mine’s haul roads will be monitored andobserved mainly while time studies are being performed inthe various trucks. In this way it will be possible to correlatepoor road conditions to increased cycle times whereapplicable, and to use this as a basis for rolling resistancevalues used in Talpac.

Simulating ideal conditions Inputs used for the various simulations are included below. Assumptions:

➤ Truck mechanical availability: 85%➤ Loader mechanical availability: 85%➤ The roster used for all Talpac simulations for the 7 day,

12 hour per day, working week that Venetia Mineoperates according to. Scheduled lost shifts are thepublic holidays that are on the South African calendar

➤ An average bucket fill factor was used: 80%.

The various possible routes that haul trucks use weresimulated in Talpac and all the information combined fordifferent cycles.

Control room Time will be spent in the control room observing the day-to-day operation of the dispatching system and, critically, howexceptions are managed and data used to support decisionsto improve real-time fleet productivity.

Truck-shovel matchingIn order to obtain the number of passes per truck–shovelcombination and the total loading time, loaders wereobserved from view points around the pit and times noted.The number of loads will also be measured while time studiesare being performed in haul trucks.

Data analysis

Time and motion studies

Results were compiled and represented graphically, as shownin Figure 3, with the reasons for deviations from the idealcycle also noted. Note: trucks numbered 8–17 are 785s,18–30 are 789s and trucks 31–36 are 793s.

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Table III

Truck and shovel matching

Truck Shovel Number of bucket passes

785 RH 200 5789 RH 200 6789 RH 340 5793 RH 340 5

Figure 3—Typical comparison of an actual cycle to an ideal one

Actual

Ideal

In the majority of cases the times that were mostexcessive were travelling times back from the dump to loaderdue to empty trucks having to wait for hauling trucks atplaces where the haul road is not wide enough for two trucksto pass one another. Pulling-off times were large in the caseof two loaders that did not have functioning hooters. Loadingtimes varied widely depending on truck-shovel matching,operators involved and rock fragmentation.

When looking at the results presented from the timestudy one may wonder what difference does a few secondsmean in a cycle of 30 minutes. In order to put a possibleimprovement in time utilization into perspective, severalcalculations were performed. Averages for all calculations aretaken for the period mid August to mid November 2005. Atime saving of 20 seconds per cycle per haul truck can save atotal of 57 days in a year for a fleet of eight 785s and twelve789s. This time can be saved at any part of the cycle; themost time can be saved on queuing—and it would exceed 20seconds a cycle.

Using averages for the time period mentioned abovequeuing can be quantified in terms of two very importantaspects—firstly tonnes that could be moved during the timewhen trucks are queuing or on standby and secondly idlingcosts. (Fuel burnt while idling was estimated at 10% ofnormal use; depreciation, repair and maintenance, labour,workshops and lubricant were included in costs.) In boththese calculations it was assumed that it was normal for fiveper cent of operating time to be spent on queuing. An extra7.2 million tonnes could be moved annually and R4.6 million

could be saved in idling costs per year for the fleet if queuingtime is used for production.

When comparing the variance (as a percentage of the‘ideal’ cycle component) across shifts it can be seen fromFigures 4 and 5 that actual queuing time differs much lessfrom the ideal during the night shift when compared to theday shift. However, the total cycle time variations are rathersimilar.

Haul road conditionsHaul road conditions were analysed using VIMS andobservations. Both haul routes—up North and south rampswere given unacceptable road ratings with the ASA(Application Severity Analysis) rating system. Below is ashort explanation of various VIMS graphs.

Strut pressure graph (Figure 6)The green line indicates machine racking; this is thedifference between strut pressures (L (left), R (right), F(front), R (rear) diagonally across the centre of the truck:(LF+RR)—(RF+LR). These motions tend to twist the frameand load machine components.

Machine bias, indicated by the blue line, is the differencein strut pressure between the totals down each side of thetruck: (LF+LR)—(RF–RR). This shows whether significantdynamic loading is occurring as the machine negotiates crossslopes and corners. Both these values should be within the 2red boundaries—racking and machine bias should be within(+/-) 8 000 kPa for 785s and (+/-) 8 500 kPa for 789 and793s.


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The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 107 NON-REFEREED PAPER APRIL 2007 219 ▲

Figure 4—Percentage variance for the various cycle components during day shift

Figure 5—Percentage variance for the various cycle components during night shift


Gear change graph (Figure 7)This graph shows the gear changes that the truck driver wentthrough from the loading area to the dump. In an idealsituation gears should be changed only when accelerating onthe way out of the loading area and decelerating beforedumping, as shown in Figure 8. Any other gear changes canbe directly attributed to uneven roadways and/or grade thatis not constant (grade breaks). The number of gear changesduring the journey is also dependent on operator skills.

Analysis on north rampThe analysis was done on truck number 30 that had apayload of 170 t. Figures 6 and 7 reflect the conditions onnorth ramp. Figure 9 shows the rating summary where roadratings range from 1–10, where 1 is the best case scenarioand 10 is the worst case scenario. Only the full analysis ofnorth ramp is included in this paper. South ramp was rated asa 10 after the same analysis was performed.

It should be noted that machine bias peaks in the redlimit on several occasions, especially at the beginning of thetravelling cycle, as can be seen in Figure 6. Machine rackremains within the recommended limits after about one and ahalf minutes of travel time.

Figure 7 points out the correlation between gear changesand speeds. It is important to note the frequency of gearchanges. With each decrease in speed increment the truck willuse more power to accelerate to its desired speed. The gearchanges cause a jerking motion, which causes spillage tooccur out of the back of the truck.

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Figure 7—Gearing up and down in truck 30

Figure 8—A graph showing the acceleration of a truck in ideal drivingconditions

Figure 6—Strut pressure graph from truck 30—North ramp

Speed

50

40

30

20

10

0

8

7

6

5

4

3

2

1

0

1 96 190

294

518

644

722

786

868

954

1025

1122

1209

1309

1460

1542

1626

1717

1903

2017

2123

2406

2653

2870

3008

Spe

ed (

kph)

Transm

ission Gear

Retarder (On) Transmission Gear

Payload = 170 (t)Datalogger Date: 2005/12/20

Truck: HA20130 Cycle: 1Gear Vs Speed: Travel Load

The number of spikes in the rack and bias graphs arerecorded and are noted in the various slots: above 8 500 kPaand below 12 000 kPa; between 12 000 kPa and 16 000 kPaand above 16 000 kPa (Figure 9). The result of the roadrating is a 6. The unacceptable range lies between 5 and 10.

The ASA ratings were confirmed with observations: haulroads, loading and dumping areas were uneven (Figure 10),thus gear changes happened often; spillage was oftenencountered en route especially at the switchback out of pitbottom (Figure 11). The haul roads are, in certain sections,not wide enough for two-way traffic (Figure 12) and at timesthe problem is compounded due, the presence of muck pilesthat have not been cleaned up (Figure 13).

Simulating results for mixed fleet operations

Simulations were run in order to determine the mostproductive combinations of trucks in the north of the pit(waste operations) and at pit bottom (kimberlite operations).Figure 14 and Table IV are results as simulated from bench11 via the north ramp to waste dump.

A visual representation of Table IV is given in Figure 14where a peak in production is shown for option four, whichconsists of nine 793s and four 789s.

Table V shows the truck combination that would takewaste out of the bottom of the pit and to waste dump viasouth ramp. The most productive option is number 10, whichincludes five 785s and nine 789s.

Thus the truck combinations for maximum productionfrom the simulations are summarized in Table VI.

Control room operations

There is currently one system controller in the control room.The person is appointed as a B-band employee, which is thesame band as the truck drivers. This ranking is according tothe Patterson Grading System where labour is divided fromunskilled (A band) to skilled, upper management positions


Paper


Figure 9—Road rating for north ramp

Figure 10—Truck leaving the loading area

Figure 13—Muckpiles along the side of the haul road, reducing roadwidth and potentially damaging to truck tyres

Figure 11—Two trucks waiting at the switchback for one loaded truckto exit the pit while dozer cleans spillage after gear change

Figure 12—Superimposed picture illustrating one of the sections ofnorth ramp which is too narrow for two-way traffic


(E and higher). The system controller is responsible for thedispatching of trucks according to the pit supervisor’sinstruction, checking in with truck drivers for diesel levelsand other technical information (this falls under fleetcontrol), answering of telephones and the truck radio

channel. The system controller is trained to understand thebasics of the dispatching system. With only one person in thecontrol room having to multitask among the mentionedresponsibilities the standard of work is likely to becompromised.

The system is able to go onto auto dispatch function. Thisis when the system collects times that it takes a truck to do afull cycle; it then averages the time and assumes that it willtake the truck that long for the next cycle. This is done for alltrucks that are being loaded by two nearby loaders. Thesystem will then optimize the trucks’ cycle times and dispatchthem to the loader where no trucks are available or whereleast queuing will take place. The method of calculating cycletimes is accurate when cycle times stay constant; however,this is not the case at Venetia where cycle times are highlyvariable as was shown earlier; thus the system will provideincorrect information when matching trucks to shovels whilethe system is on auto dispatch. This is evidenced by the factthat queuing is still frequently observed when auto dispatchis utilized—in part due also to truck-shovel matching andloading times.

Truck-shovel matching

Table VII below shows an example of truck-shovel matchingobservations. A wide range of total loading times can beobserved. It must be noted that the shovel operator remainedthe same throughout this particular study. All trucks excepttruck 14 are 789s, which are loaded in 5–6 passes. Shovel308 should be capable of a maximum of 5 passes (4 ideally)with this truck-shovel combination.

With the recommended time of 30 s per pass, loadingtimes often go beyond the recommended time of 2 minutes30 sec (5 passes @ 30 seconds per pass). The RH340 shovelshould be able to load a truck in 5 passes; however, sixpasses were needed in many cases. This is often dependenton loader operator ability and on the fragmentation of therock. The pull-away time ranges from 5 to 52 seconds. Thiswas often due to loaders 306 and 307 not having hootersavailable to give truck drivers the go ahead that the loader isfinished loading. The other reason that pull away takes arelatively long time is due to the loading area conditionsdescribed earlier. Attention is required both from the point ofview of the truck (drivers do not spot in the correct place nextto the loader) and the loader (loader operators have toconstantly adjust to different truck sizes and thus misplacethe load).

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Table V

Truck combination exiting south ramp

Option 785 789 Million tonnes

1 7 2 11.222 8 2 11.963 9 1 11.604 9 2 12.455 9 3 13.026 8 4 13.347 5 6 13.388 5 7 13.589 5 8 13.7310 5 9 13.84

Figure 15—Graph of the truck combination exiting south ramp

Table VI

Truck configuration for maximum production

Amount Truck model Material Exit Dump

5 785 Kimberlite South Crusher

7 789 Waste South Rugen waste dump

4 789 Waste North Rugen/Kronewaste dump

9 793 Waste North Rugen/Kronewaste dump

Total trucks: 25

Mill

ion

to

nn

es p

er y

ear

mo

ved

Option

Talpac simulation

Table IV

Truck combination exiting north ramp

Option 793 789 Million tonnes per year

1 9 0 21.952 9 2 22.343 9 3 22.394 9 4 22.465 9 8 22.416 8 8 22.407 9 11 22.37

Figure 14—Graph of the truck combination exiting north ramp

Mill

ion

to

nn

es p

er y

ear

mo

ved

Options

Talpac simulation

Conclusions

The split shell mining method used at Venetia Mine doesimpose certain road restrictions; however, it is still veryimportant to construct and maintain haul roads at correctwidths, even and free of spillage. Parts of the cycle that canbe most improved upon include decreasing spotting, loading,pull-away and queuing times. Hauling times are most oftenon a par with an ideal simulation but empty travelling timesare often longer than the ideal as a result of the truckshaving to wait for hauling trucks due to roadways being toonarrow. The control room employee has many responsibilitiesto focus on while he/she is alone in the control room duringthe shift. This decreases control room efficiency. Trucks andshovels are well matched but loading times vary considerablymainly due to operator efficiency and rock fragmentation.


The idealized cycle times and those actually recorded showedslight differences in most activities, except for unladen traveltimes and queuing times, where significantly longer cyclesegment times were recorded. Additionally, the pull-awaytime of trucks is excessive; the reason for this was identifiedduring the time study, i.e. two loaders did not have hootersand therefore on completion of the final pass, the trucks didnot pull away immediatley but waited for visual or radioinstructions. The time spent queuing at the loader, due toincorrect dispatching or loader downtime itself can bequantified in terms of extra tonnes that can be moved as wellas savings in unnecessary idling costs.

Haul road conditionsHaul roads in the pit are not maintained to standard.Incorrect haul road width at places causes bottlenecking andan increase in truck travelling times. The presence ofswitchbacks causes trucks to have to stop and wait for eachother as they are not wide enough for two trucks to pass.Although temporary in terms of mining, ‘temporary’ rampshave a permanent effect on production, i.e. they should be aswell constructed (especially width and turning radii) andmaintained as permanent ramps. This will increase the speedof trucks on the laden haul and contribute to reduced cycletimes.

Simulating ideal operating conditionsTalpac simulations indicated that it is possible to run theoperation optimally with a smaller fleet than is currentlyavailable. Trucks will, however, not be parked but rotatedwith those that are in for repair and servicing. It is importantto note that the economic implication of this decision has notbeen analysed here. However, it is also important to note thatunder mixed fleet operating conditions, more trucks in thefleet of various sizes (when loading units are not aloincreased) does not necessarily result in commensuratleymore tonnage production, queuing time increases and, due todifferences in engine power per ton gross vehicle mass, traveltimes may slow to accommodate the slowest truck on a rampat any given point in time.

Control room operationsThe control room serves as a focal point for improving mineproductivity. Consideration needs to be taken whether one


Paper


Table VII

An example showing split times for each bucket pass for loader 308

Grey highlighted blocks represent good spotting times


person can effectively deal with all the requirements or ifchanges should be made to the control room staff, or how theinformation received by the control room is reviewed andused as a basis for optimal management of the operatingfleet.

Truck-shovel matching

Trucks and shovels should be matched as per abovediscussion in order for loading times to be reduced. Theresults show that there is room for improvement and thepossibility remains to load a 789 with a RH 340 or 785 witha RH 200 in 2 minutes or less. The variability of bucket cycletimes indicates variable fragmentation or muckpile conditionsand the impact of diggability on these times should be furtherinvestigated.

Acknowledgements

I would like to thank De Beers for the opportunity to completethis project at Venetia Mine and for allowing the findings tobe published.

References

CATERPILLAR GLOBAL MINING EQUIPMENT MANAGEMENT. A Field Guide to MiningMachine Application. Version 1.05. 2002.

GALLAGHER, M.S, and KEAR, R.M. Split shell open pit design concept applied atDe Beers Venetia Mine South Africa Using the Whittle and GemcomSoftware. De Beers Consolidated Mines. GIBS—Executive DevelopmentProgram Final Project. 2002.

MACLEOD, J. Personal communication. 2005.SMART, K. Personal communication. 2006.SMART, K. Efficient Haul Roads Presentation 2002. ◆

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