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Haul Route Design & Condition

Haul route design can significantly affect performance and efficiency, particularly in the case of mechanical trucks. Increases in grade away from a design of 10% can cause trucks to change down often getting stuck in 1st gear or requiring a site to lock into 1st gear, dramatically reducing both productivity and efficiency. Sub-optimal planning can result in longer than required hauls or avoidable corners. MaxMine provides tools that regularly identify these issues. Haul road condition and rolling resistance also have significant impacts upon, carbon intensity, productivity, asset and human health. Soft ground and high rolling resistance consumes energy, increasing fuel burn and reducing speed. Rough roads cause speed variation, with braking and re-acceleration wasting fuel and cycle time and often causes mechanical trucks to run at high RPM in lower gears, increasing fuel consumption. Rough roads reduce asset and tyre life, while also affecting drivers, reducing speed and in some cases, causing injury.

Haul Truck Operators

Haul trucks operator performance typically varies between 30-50%. MaxMine has over 5 million hours of haul truck operator data proving the utility of our haul truck operator league in safely increasing operator speeds. MaxMine provides section by section feedback to drivers, scoring performance to ensure maximum speeds are maintained, within a strict safety framework and without impacting asset health. MaxMine Carbon incorporates carbon impacts into this scoring method, giving operators a direct measure of the carbon footprint caused by their driving style. Typically, faster, smoother drivers who maintain momentum, use significantly less fuel. MaxMine Operator lifts haul speeds by 10-15% while reducing fuel consumption by up to 6%.

Load Unit Operators

One of the best ways to reduce carbon per tonne is by increasing the tonnes moved on each haul. MaxMine Carbon load operator league scores operators on payload and load bias compliance, and provides feedback on actions to improve their score. Trust in the scores is critical: MaxMine calculates payload using direct strut pressure measurements and a physics model of the truck suspension geometry. Performance of the MaxMine payload algorithm over the past 6 years repeatedly show less than 2% error compared to blind tests against weighbridge results. Dig unit operators can further reduce fuel consumption via well planned operation to reduce clean-up fuel burn and truck queueing (idle).

Dispatch

In the carbon driver tree, idling during production has been split from other idling events. Productive idling includes queuing, loading, dumping and idling at intersections. Generally the only component of these idling types that is controllable is the queueing. On most mines queueing is controlled by either a dedicated dispatch team, or on smaller sites, by the production team acting in a dispatch capacity. MaxMine Carbon quantifies the carbon footprint by leveraging the automated MaxMine activity classification to identify all idling events without requiring human input. Carbon driven dispatch KPI’s can then be included in the decision making process, free from interference by human input errors.

Operational Discipline

While dispatch is largely responsible for idle while queueing at dig units, idle time occurs in other areas including, waiting at the go lines, at dumps, one way roads or other traffic constraints. Given far higher fuel usage rates, unnecessary off haul travel contributes to significant fuel wastage. MaxMine provides automated idle identification and tools for dispatch and production supervision to minimise all idle time.

Mine Planning & Off Haul Travel

Off-circuit travel can be substantially reduced through informed planning. Strategies such as moving go-lines to facilitate faster changes between circuits or even moving them in-pit, can substantially reduce the need for haul trucks to travel off-circuit. The increase in available time for production is well known in the industry but MaxMine Carbon also quantifies the reduced carbon footprint. While mine planning has been listed here, in an ongoing operational mine off-circuit improvement projects are of course a collaborative effort between planning, production and dispatch. MaxMine provides automated off-haul identification and tools for dispatch and production supervision to minimise off-haul travel time.

Asset Health

Engine health can substantially impact fuel usage and hence the rate of carbon released to the atmosphere. MaxMine is capable of measuring high rate data from all OEM engine sensors, which is analysed to detect issues such as clogged air filters, under-performing turbos and injectors as well as dragging brakes. Equipment can then be ranked by excessive fuel usage to help prioritise carbon-based maintenance actions, supported by automated diagnostic assistance and targeted performance data. By using machine learning, MaxMine carbon also separates asset health from other effects such as operator usage meaning the overall opportunity for carbon reductions through maintenance actions can be quantified. One new sites, MaxMine typically identifies at least 20% of trucks with an efficiency deficit of 10% or more.

Asset Selection & Configuration

Diesel electric trucks and mechanical trucks have key differences than can provide relative benefits in differing mine sites. For example, electric trucks have higher drive train efficiency and can deliver maximum power regales of grade, however they burn fuel on the declines, where mechanical trucks under engine braking, do not. Electric trucks allow for the option of (renewable) overhead power, whereas mechanical trucks do not. Specialised modelling can help make the optimal truck selection. Some truck or dig unit engines provide options for fuel calibration, fuel optimised (higher NOx, HC) or NOx, HC optimised, (lower fuel efficiency – higher CO2 output). Calibration selection should be carefully considered, noting the higher NOx and HC of fuel optimised options. Other options to reduce fuel burn include, for example, battery powered cabin air conditioners, to avoid engine idle.

Production

Total machine productive time per shift is directly influenced by the time between last load and first load, ramp-up in loads per hour and the drops in total machine hours from smoko and lunch. Collectively these effects can be referred to as the rhythm of a shift and are often translated into KPI’s for the production team who control these aspects as part of normal operations. MaxMine Carbon can help quantify these effects and augment the usual KPI’s listed above in terms of relative carbon footprint per tonne produced.

Haul Trucks

EH4500-2
EH4500-3
EH5000-3
EH5000AC-3

Load Units

EX1200
Ex1200-6
EX1900-6
EX2600

EX3600
EX5500
EX5600

Haul Trucks

777D
785B
785C
789A
789C

789D
793C
793D
793F

Load Units

992C
992D

Other Equipment

Motor Graders
16M
24
24M

Water Trucks
773E
785B

Dozer
DT10

Haul Trucks

T264

Load Units

R9350
R9400
R9800
R996B

Haul Trucks

830E
930E

Load Units

PC5500
PC7000
WA1200-3

Optimising Payloads

Context

Our client, a Gold mine in Western Australia operated by a large multinational miner, was suffering from a high degree of variation in haul truck payloads across the operation. This resulted in lower than target average payloads and lower total material movement, while still triggering a number of overloading events, which impacted not only the health of the trucks themselves, but also their productivity.

MaxMine Application

Together with the site engineers and maintenance team, MaxMine was able to complete an “optimal payload” study for the site, which indicated that the site’s payload target could be increased significantly. However, to prevent an increase in the frequency of overload events, we first rolled out MaxMine’s operator scoring metrics for the shovel operators. MaxMine automatically and objectively measures each operator on their conformance to payload targets (both over and underloads) as well as loading bias and loading time. This helped to reduce the payload variation between loads and allowed the site to increase payload targets. Once variation was suitably controlled, we worked with the site team and the OEM to further increase payload targets in line with the optimal payload study results.

Client Results and Discussion

8% increase in average payload
Improved operator conformance to loading metrics, including loading time and load bias
Improved visibility on site around overload events and reduced the frequency of these events

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Custom Dashboard to Reduce Queue Time

Context

A client was exceeding their budgeted engine hours while not achieving target tonnes hauled. Pursuant to this, an in-field observation suggested that queuing was occurring, however, data from the fleet management system could not substantiate this observation. Management suspected that dispatch operators were manipulating their KPI’s by manually changing delay codes (queuing) to standby codes (waiting by shovel).

MaxMine Application

MaxMine began by automatically distributing customised dashboards to make the magnitude of the queuing problem visible to all stakeholders. Parallel to this, MaxMine coaches were deployed to aid the client in improving how queue times are managed during normal operations. The coaches reviewed and updated standby and delay codes to improve efficiency with which the fleet management system allocates trucks. To ensure long term sustainability the dispatch team were trained as to how to manage queue times during the shift. With MaxMine’s implementation and changes the client was provided with a single version of the truth, thereby eliminating the loophole to manipulate KPIs.

Client Results and Discussion

16% reduction in overall queue time while total number of hauls remained the same
Annualised savings of $2.4m ($240k saved in the first 35 days post implementation)

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Reducing Cycle Times By Improving Operator Productivity

Context

One of our clients, a large open pit gold and copper mine, was suffering from high variation between haul truck operator average haul speeds. This was being driven largely by operator behaviour due to a lack of a consistent and objective metrics to measure and manage individual operator performance. This not only caused higher average cycle times, but also caused bunching within the circuits.

MaxMine Application

Our trusted and unique methodology helped to significantly reduced variation in operator performance. In conjunction with management, our dedicated site coach helped our client communicate the new way of measuring performance to the operating crews to make sure that they all had a solid grasp of what they needed to do to achieve their targets. Our performance management approach is grounded by organisational psychology principles, and MaxMine’s operator efficiency metric epitomises this. It was deployed to automatically and objectively measure, compare and improve individual operator performance. Operators were able to view their own performance scores against those of their peers via wallboards installed in the crib rooms, displayed in a league table format to gamify the results. Subsequent further data analytics available to each supervisor and each operator allowed for targeted operator performance insight to manage gear selection, throttle and retarder usage.

Client Results and Discussion

Sustained increase of 12% in average full haul speed
13% increase in average return haul speed
Sustained improvement in haul truck operator safety and productivity scores
Maintained for 6 months after target attainment
Measured across all crews and shifts, normalised for haul distances and elevation

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Fixing An Under-Performing Truck Of 2 Years

Context

A tier 1 mining site partnered with MaxMine to deliver the next level of operational efficiency desired. Truck ‘DT06’ was on average 1.8kph (or 1 minute per cycle) slower than the rest of fleet up inclines. Following multiple unsuccessful maintenance interventions, it was decided to park the truck for backlog maintenance work.

MaxMine Application

With backlog work completed DT06 was sent back into operations. Using MaxMine’s equipment health diagnostic dashboard, the MaxMine team immediately reported that DT06s performance had not improved despite maintenance reports indicating otherwise. Our dashboard mostly agreed with in-field test results except for the fuel flow which was 60lph less than the remainder of the fleet at 1750rpm. Due to a higher data resolution, MaxMine was the key enabler for both the maintenance and production teams to agree DT06 remained an underperforming truck.

Client Results and Discussion

The diagnostic workshop with maintenance crew identified 3 priority fixes with priority 1 outcome completed within a half hour. Reviewing the truck files confirmed the ECU was incorrectly flashed and remediation work was completed by the next shift with confirmed improvement that evening
MaxMine is now fully integrated into scheduling activities to improve asset health

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Data Driven Safety And Incident Investigation

Context

The engine of a fully laden Cat 793 stalled while travelling up an incline, remained stationary for 2.5 minutes and then rolled backwards reaching a maximum speed of 17.5 kph before hitting a windrow and coming to a stop. Thankfully no one was injured during this incident. Existing OEM/site diagnostic tools did not register any fault codes during the time that the incident occurred, nor did the machine produce any alerts. This created some confusion as to the root cause of the issue making it difficult to put measures in place to avoid similar incidents in future. Given the severity of incident the mine’s inspector had closed the operation down until they were able to demonstrate that the rest of the fleet were not prone to the same concerns.

MaxMine Application

Using our unique algorithms MaxMine engineers were able to produce a black-box style incident investigation report within 24 hours of us being notified of the incident. We successfully identified the root cause for the failure – a loose wire on the ECU causing intermittent fuel loss lasting a tenth of a second.

Client Results and Discussion

Maintenance crews were provided with pin-point accuracy on what the fault was and why it was occurring
Management and site crews were able to put measures in place to avoid similar incidents in the future
The same algorithm was used to confirm the fault did not exist on other equipment

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Gold Mine Tyre Life Improvement

Context

A fleet of 180 and 220 tonne trucks deployed in a large open pit gold mine began to experience very low tyre life. In turn, tyre cost per tonne soared which resulted in not only increased operational costs, but also a large amount of downtime caused by frequent tyre change outs.

MaxMine Application

Implementing MaxMine and our performance metrics to manage tyre life, such as cornering speed, resulted in on-site leadership and a MaxMine roll out team communicating goals to front-line supervisors who then communicated the goals to operational teams. Step by step coaching support was provided to ensure that improvement based behaviour was conveyed to teams. The feedback to crews and supervisors was automated and embedded as part of daily KPIs via the MaxMine interface by installing wall boards which displayed operator performance in terms of their cornering speeds in a league table to encourage healthy competition between operators as well as simply making scores visible to the crews on site.

The Result

A 75% decrease in tyre spend was achieved using MaxMine’s operator tools to improve cornering speed conformance.

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Focus on Suspension to Improve Availability

Context

A large open pit coal mine began to experience frequent damage to truck frames. This damage lead to failures which in turn created significant amounts of downtime and expensive repairs. This also resulted in severely erroneous payload measurements by the trucks on-board weighing systems which rely on suspension strut pressure data, amount other metrics.

MaxMine Application

Following the implementation of MaxMine, it was determined that overcharged suspension cylinders were a root cause of these failures, specifically the incorrect recharging procedures. A dedicated crew of artisans and technical specialists recharged and reset ride heights along with re-calibrating payload measurements of all the machines in the fleet. MaxMine established mechanisms to remotely measure strut pressures before and after recharging and having analysed the data the correct procedure for recharging was formulated.

Client Results and Discussion

12% increase in machine availability
91% reduction in suspension related alerts generated daily

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