Highvale optimizes truck fleet productivity with Argus 

Canada’s largest surface strip coal mine increased the effectiveness and productivity of its truck fleet by up to 20% within six months by adopting MineWare’s Argus shovel monitoring system.

Highvale is one of three TransAlta-owned coal mines and is Canada’s largest surface-strip coal mine covering more than 12,600 hectares. In operation since 1970, the Highvale Mine is located 70 km west of Edmonton on the south side of Lake Wabamun and supplies TransAlta’s Sundance and Keephills coal-fired power plants with sub-bituminous, low-sulphur coal.

Highvale is one of three TransAlta-owned coal mines and is Canada’s largest surface-strip coal mine covering more than 12,600 hectares. In operation since 1970, the Highvale Mine is located 70 km west of Edmonton on the south side of Lake Wabamun and supplies TransAlta’s Sundance and Keephills coal-fired power plants with sub-bituminous, low-sulphur coal.

The challenge

SunHills Mining sought to boost productivity, better utilize their equipment and accurately track performance metrics for continuous improvement. 

Highvale mine operates four draglines, two electric and four hydraulic
shovels with a mixed fleet of 25 haul trucks and various support equipment.
This case study focuses on the Caterpillar 495HR and P&H 4100XPC electric
shovels loading Komatsu 930E, Terex MT4400AC and Liebherr T282B
trucks.

The solution

The Argus shovel monitoring system from MineWare offered a fully integrated solution for truck/shovel optimization, truck payload compliance and machine health. 

With truck hauling being the largest operating cost in most mining operations, modest improvements to the truck/shovel fleet would lead to substantial savings in lower production costs.

This innovative approach to monitoring shovel loading enabled a payload accuracy of 3% compared to other truck-side payload methods that are typically only accurate to 10-15%.

Unlike the truck scale systems, Argus provides operators and technical support teams with the assurance of reliable payload and automated time usage tracking that other competitive systems could not provide.

argusshovelmonitor

Payload monitoring = haulage improvements

Haulage is usually the largest operating cost for a mining operation, requiring careful management.

MineWare’s Argus system is designed to improve surface mining operations through high precision shovel and bucket location reporting, leading to a reduction in operational costs and an increase in overall productivity.

Irregular truck loading can result in a number of costly impacts such as truck bunching leading to material spillage, significant road damage and time lags, higher haul truck maintenance costs, premature tyre failure and higher fuel costs.

 At the other end of the scale, an under-loaded truck can also reduce a mine’s productivity. For example, the consistent under loading of five trucks in an operation by just 20%, is equivalent to under-utilizing one whole truck, which is costly to an operation.

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INCREASED EFFICIENCY & PRODUCTIVITY

  • Greater shovel production through enhanced operator efficiency and output.
  • Increased productivity through increasing payload compliance in the truck fleet.

DECREASED OPERATIONAL COSTS

  • Reduced truck maintenance by decreasing over loads.
  • Increased mine compliance by reducing costs by only moving planned dirt.

The results

In the six months that Highvale has adopted the Argus system, the mine has experienced measurable increases in productivity, performance and payload compliance while also seeing a reduction in the stress/tonne on the shovel during production.

The below statistics compare a base month, the first month with the system full operational against the results of the following six months.

Using this methodology,  Argus produced the following results:

  • Increased cycling productivity by 8%
  • Increased the % of compliant trucks by 20%
  • Reduced stress/tonne on the shovel by 6%

To understand how these results were achieved it is necessary to break down the shovels performance into its separate components. Results are captured in more detail below across payload compliance, shovel productivity by pass, operator productivity and machine stress. 

Payload Compliance

The below graphs illustrate the change that occurred from the first base line month to the sixth month.  Changes can be seen not only in the increase of compliant trucks, but also in a decrease in severity of under loads and decrease in frequency of major overloads.

shovelpayloadcompliancebaseline
month6payload

Key Progress:

  • Reduction in underloads of 19% from 31% to 12% of total loads
  • Increase of compliant trucks by 20% from 55% to 75% of total loads
  • Reduction in frequency of major overloads by 2.4 times from 2.28% to 0.95%
  • Improvement of 6% in average severity of underloads from: 79% to 85% of truck TSL

These results offer tangible improvements to the mine site but do not demonstrate how the shovel produces to fill the trucks.  To understand that, productivity statistics must be looked at both by shovel pass and shovel operator.

The above chart displays the % of passes across the case study period, the division of passes would be as follows (F=First, M=Middle, L=Last): 2 Passes: F-L 3 Passes: F-M-L 4 Passes: F-M-M-L 5 Passes: F-M-M-M-L

The above chart displays the % of passes across the case study period, the division of passes would be as follows (F=First, M=Middle, L=Last):

2 Passes: F-L
3 Passes: F-M-L
4 Passes: F-M-M-L
5 Passes: F-M-M-M-L

Shovel Productivity by Pass

By deconstructing the shovel’s loading cycle into first, middle and last pass, we can gain key insights into the effect Argus has on a shovel’s productivity.

Using the information captured by Argus, we can independently analyse loading cycles to better understand the source of productivity improvements.

The following analysis shows the performance trends of the different shovel passes month-by-month following the adoption of Argus.


firstpass.png
lastpass
middlepass.png

*Productivity improvement is based on total tonnes moved/total cycling time.  
Cycling time is equal to swing time + fill time + dump time + return time

 

    FIRST PASS: the first bucket the shovel loads into the truck.  

    Generally the first pass has the largest payload because its cycle time is not governed by the shovels speed, but rather it is limited by the truck backing into the shovel. This gives the operator ample time to fully load the bucket to capacity while it waits for a truck to manoeuvre into position.

    Key Progress

    • Cycle Time: Increased by 1.7%
    • Payload: Increased by 8.3%
    • Productivity*: Increased by 6.5%

    LAST PASS: the last bucket that goes into the truck before it is signalled out

     The last pass differs from the first in that it is mainly limited by payload and not cycle time. Using Argus, a clear target is displayed to the shovel operator after each pass, showing how many tons should be loaded in each truck.  Argus provides the operator with the ability to judge the best last pass fill for optimum payload compliance without impacting the speed of the shovel cycle.

    Key Progress

    • Cycle Time: Decreased by 2.8%
    • Payload: Increased by 5.5%
    • Productivity*: Increased by 8.6%

    MIDDLE PASS: this category is any bucket that is neither the first nor the last. 

    It is an exception to the above two categories because it has no constraints. Both its cycle time needs to be minimized and its payload needs to be maximized, meaning it is the clearest reflection of an operator’s loading skills because there are no constraints.

    Key Progress

    •  Cycle Time: Decreased by 0.4%
    •  Payload: Increased by 11.2%
    •  Productivity*: Increased by 11.7%

     


    As shown, the improvement profile differs between the types of shovel passes.  However, with a final productivity improvement of 6.5%, 11.7% and 8.6% on the first middle and last pass respectively across all categories, Argus has demonstrated proven results.  Productivity improvement is based on total tonnes moved/total cycling time.  Cycling time is equal to swing time + fill time + dump time + return time

    Operator Results

    Individual operator performance is also important to consider.  No two shovel operators are the same nor do they have the same level of acceptance to new electronic aids and technologies.  Analysing the effect of Argus on the different operators can help predict how effective Argus can be adopted by different mine sites and work cultures.

    The following analysis focuses on operator engagement and how Argus can help improve their behaviour and competencies. The benchmarks compare the most productive operator and the least productive operators to the overall average of the machine.  The payload system is also capable of reporting a cycle-by-cycle stress factor that indicates how smooth or rough they handle the shovel.

    All operators showed improvements over time, challenging the misconception that only the lowest performing operators can improve. This also shows that the most productive operators have the largest performance gains in the shortest amount of time.

    All operators showed improvements over time, challenging the misconception that only the lowest performing operators can improve. This also shows that the most productive operators have the largest performance gains in the shortest amount of time.

    Another consideration that needs to be looked at while attempting to boost the productivity of a machine is the impact on machine health. If only production is considered, the operating availability of the machine can suffer, resulting in an overall deficit due to lost digging time.

    The chart below illustrates that even while improving productivity the stress on the machine per tonne loaded can be reduced.

    machinestress.png
    Provides Real-Time Feedback to the operator .jpg

    The results

    argusoperatorscreen

    All operators demonstrated clear improvements in productivity and a reduction in machine stress.  In addition to tracking statistics, operators at Highvale have also devised ways to leverage Argus to improve the process—ranging from using the system’s weather forecast to plan cable routes to quantifying different dig plans with the data available to them in the cab. Argus has quickly become a highly adopted tool across operators at Highvale.

    A 6 Month Story          

    Highvale has demonstrated great success within six months of introducing Argus. The mine site has increased the effectiveness of its truck fleet, decreased its stress per unit produced and increased its productivity.

    There is now a continued effort to adopt Argus at more levels on site. Future plans include:

    • Directly integrating Argus into Highvale’s enterprise management system
    •  Utilizing the data from Argus for daily operator reviews
    • Gaining operator specific feedback to target the lowest performing operators
    • Implementing production alarms to maintain standards and aid dispatch

    As a result, Argus has provided Highvale with an excellent tool to improve productivity and efficiency in today’s challenging global mining climate.

     

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