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Small steps to big savings

Published by
World Coal,

Derek Cooper, Motion Metrics, Canada, provides practical cost-saving solutions for opencast coal mines.

Coal mining is a very labour intensive process. Removing overburden, cleaning coal seams, and loading the coal all require large amounts of work and energy. Although mining equipment has become bigger and more advanced over the years, mining labour productivity has decreased continually since the early part of the millennium.

Figure 1 shows the rapid decline in global mining productivity despite investment in newer and larger equipment (the traditional solution). As shown in Figure 1, the advanced mining regions of North America and Australia have not fared any better than the rest of the world.

Industry observers will be quick to point out that the usual suspects (stricter government regulations, increased corporate oversight and overhead, changing labour attitudes, and generational differences) are all possible contributors to the global decline in mining productivity. However, a less obvious – but nonetheless significant – culprit is geological in nature. Much like Hubbert’s ‘peak oil’ hypothesis, which proposed that, without technological advancement, all of the easy-to-access oil has already been extracted, the mining industry is also struggling with incrementally higher strip ratios (Figure 2).

Comparing the 4 – 6% compounded annual decline in global productivity in Figure 1 with the 4% increase in strip ratios in Figure 2, there appears to be a reasonable correlation between the two.

This editorial makes no political or societal judgements and, whether the main driver of declining productivity is higher strip ratios or bloated support and management structures is insignificant. It is clear that declining mining productivity is a problem, and mines need to find ways to increase productivity.

In unpredictable pricing environments, coal mines must focus on unit cost reduction to survive. In an analogy to Hubbert’s theory, most of the low hanging fruit has already been picked, so the geology is working against the industry. Societal, political and labour attitudes are undoubtedly an issue that the mining industry must deal with on a large scale to offset the observed productivity declines. However, the simplest solution within the grasp of mining leadership is to find methods of sustainably increasing mining productivity (equipment/labour) through improvements in technology and a focus on best practices.

Fix the most expensive item first: truck-and-shovel prestrip fleets

When attempting to achieve maximum cost savings, it makes sense to focus on the highest cost units first. Truck-and-shovel costs per banked cubic metre (BCM) are typically 3 – 5 times the cost of material moved by a dragline.3 Although flexible and mobile, truck-and-shovel fleets require significant quantities of diesel fuel, labour hours, and maintenance effort when compared to draglines. In addition to the significant cost of truck-and-shovel operations, the effectiveness of these fleets is falling faster than any other mining method, as depicted in Table 1.

The seriousness of both increasing strip ratios and falling truck-and-shovel productivity should not be taken lightly. One of the subtle implications of increased strip ratios is that mining methods are shifting increasingly towards truck-and-shovel operations. Even mines that have traditionally used draglines now require single or multiple prestrip fleets. The significance of these are rapidly raising prestrip removal costs, which disproportionately affect overall unit costs.

One solution: getting the most out of every truck and every bucket

Until mining has its ‘fracking moment’, a revolutionary technological advancement that drastically alters mining productivity, small evolutionary (continuous improvement) advancements should be pursued. Prestrip operations have two complexities: maximising shovel productivity and matching that rate to the truck fleet.

Seasoned shovel operators are often better at the former than the latter. As shovel productivity increases and trucks become scarce, operators tend to compensate by overfilling trucks. Every field engineer has observed a heaped truck struggle to get up a ramp, spilling its payload over the sideboards.

Productivity is the goal, but not at the expense of truck damage. Complying with the OEM’s 10-10-20 rule is imperative. Simply put: the trucks may only be overloaded by 10% up to 10% of the time, with no single load exceeding 20% of rated payload.

According to PwC’s productivity database, 93% of trucks breached the OEM rule.5 The goal of payload compliance is to maximise the usage of each truck without causing damage, downtime, or voiding the OEM warranty. Overloading a truck significantly increases the risk of structural damage, requiring lengthy downtime and costly repairs. For example, losing a truck to structural welding can easily offset the additional productivity gained in the short term through overloading.

There are several common tools to provide shovel operators feedback on their bucket loads and payload in order to optimise each load while complying with OEM specifications.

Payload monitoring tools: optimising every load and every bucket

Weighbridges and truck scales are the gold standard in payload measuring and considered the most accurate method. However, they lack several key functions. Most operations do not have permanent below-ground scales; they are normally temporary, for calibration or periodic study purposes, and they do not provide bucket-by-bucket feedback to the shovel operator.

The temporary nature of most truck scales, as well as their common placement at great distances from the shovel, reduces the usefulness of the training feedback mechanism. In addition, the potentially overloaded truck must drive to the scale before the issue is even recognised.

Truck-mounted payload displays are another set of common tools used to measure payload. These have advantages over weighbridges in that they are permanently affixed to the truck and visible to the shovel operator, which supports training feedback. However, there are also numerous disadvantages to these systems. Firstly, payload measurement occurs after the truck has already been loaded. This is a very reactive response to an overload; the truck has already incurred overload stress and must quickly dump its load, incurring rehandle. Typically, the truck also has to drive a short distance to activate the system to provide a reasonably accurate payload estimate and there are many strut-based sensors that must be routinely calibrated to maintain accuracy. The shovel operators must also continually watch the scale and perform mental arithmetic to estimate their bucket-by-bucket performance. Lastly, truck-mounted payload displays may lead to inconsistent measurements, as each individual truck may be calibrated differently.

Recent developments in robotics, estimation theory, and sensor technologies now allow for direct payload measurements on mining shovels and excavators. These have the advantage of immediate shovel operator feedback, a limited number of sensors to maintain and, most importantly, provide instant feedback on the shovel bucket before the load is placed in the truck. Knowing the payload before loading a truck is the best method for getting the most out of each bucket and eliminating overload situations; it is preventative rather than reactive. Finally, in a typical opencast mining operation, each shovel will load a fleet of four to eight haul trucks. Hence, maintenance is much more manageable on one shovel-based system than four or more truck-based systems.

Focusing on payload monitoring (PLM)

Motion Metric’s Shovel Metrics™ system uses high-precision orientation sensors to determine the exact posture of a hydraulic shovel arm and pressure sensors to determine the force being applied by its cylinders to lift the load. This technique, which is based on concepts from advanced robotics and estimation theory, determines the payload in real time without interrupting the normal flow of operation (Figure 3), and has been proven accurate within 2% of the weight scale verified payload.6

Using the solid-state orientation sensors, joint angles are measured at its linkages, including cab, boom, stick and bucket, if possible. Depending on the specific shovel type, various pressures are measured to estimate net force output from the hydraulic cylinder(s), further converted into torques with known shovel arm geometry. Kinematic calibration ensures that the arm geometry is correctly measured in real time, while dynamic calibration ensures that only the payload inside the bucket is weighed. A known weight calibration usually follows to further fine-tune the payload measurement outcome.

To further explain, hydraulic pressures inside the shovel cylinders are affected by the high weight of the metal arm links (boom, stick and bucket), the posture of the arm (statics), the movement of the arm (dynamics), the statics and dynamic friction inside the cylinders and at the joints, and of course the weight of the dirt inside the bucket. Hence by using advanced robotics theory, all those effects need to be compensated for in order to measure the external payload weight.

The sensor measurements are read at 30 Hz using a rugged embedded computer running the PLM algorithm. The algorithm includes a machine that identifies which state the shovel is at. The shovel could be digging, lifting/swinging, dumping, returning or idle. Knowing the state, the real-time measurements are carefully filtered and fully compensated for the high-vibration digging environment. The result is a dynamic estimate of the payload inside the shovel bucket.


In order to test the effectiveness of the Motion Metrics payload monitoring system, it was tested in a large precious metal mine in Africa on a Terex RH200 and Terex MT4400 with a rated capacity of 220 t. The PLM system was compared to both onboard truck scales, as well as a weighbridge.

The PLM system was found to be comparable to the weighbridge in terms of accuracy, as shown in Figure 4, while the onboard truck scales displayed consistently under-reported payload numbers. This is significant for several reasons:

  • Replicating weighbridge accuracy, while being visible to the shovel operator, supports real-time training and feedback.
  • As a substitute for onboard scales, the PLM system can reduce maintenance calibration time and effort.
  • The under-reported payload of the onboard scales during this study would lead the shovel operator to inadvertently overload trucks on a consistent basis.

In fact, once the payload system was commissioned on the production fleet, the magnitude of the accidental overloading became immediately evident.


After a month of using this PLM system and focusing on compliance, several operational benefits were immediately realised (as noted in Figure 5):

  • Improved bucket fill (left-hand side of Figure 5): note that the average bucket fill had improved by ~10%, reducing the total number of buckets to fill each truck.
  • Improved payload compliance (right-hand side of Figure 5): the curve has shifted to the left, reducing the number of overloaded trucks, as well as the amount by which each truck is overloaded.

The most evident trend is that the overloads, greater than 110% rated capacity, have declined by 31%. Secondly, the percentage of compliant loads (95 – 110% of OEM rated capacity) almost doubled, increasing from 12% to 22%. With increased compliance, the total monthly production of the mine did not change, as the increase in shovel productivity had offset the reduction in per truck payload. The mine had been able to increase shovel productivity and reduce potential damage and downtime on their truck fleet, all while maintaining the overall mine production.


The decline in mining productivity is especially pronounced in truck-and-shovel fleets, so it is critical to control the costs of this relatively expensive mining function.

Overloading trucks is not the best path to overall gains in material movement, as the downtime due to unplanned maintenance can easily offset the uptick in short-term productivity. To maximise the productivity of prestrip fleets in a sustainable way, the operation needs to maximise truck payload within the OEM recommendations and focus on efficient loading procedures.

For optimal performance, payload data needs to be visible to the shovel operator who controls the loading of the truck. As noted in the initial trial, an inaccurate tool is potentially worse than no tool at all, especially if it is under-reporting tonnages. Bucket-by-bucket payload monitoring, on the other hand, provides actionable information to mine management, allowing them to adjust their loading and hauling strategy in order to maximise overall mine productivity.

A shovel-based system is the preferred method for accuracy, training, and ease of maintenance. The use of the Motion Metrics shovel-based payload monitoring system at an African precious metals mine increased the occurrence of compliant payloads from 12% to 22%, reduced the number of severe overloads by 31%, increased shovel productivity by 10%, and did not reduce overall mine production.


  1. Price Waterhouse Cooper. Mining for efficiency. PwC. (August 2014).
  2. MUDD, G. ‘The Sustainability of Mining in Australia: Key Production Trends and Their Environmental Implications for the Future’,
  3. Department of Civil Engineering, Monash University and Mineral Policy Institute, (2007).
  4. NEL, R. and KIZIL, M. S. 'The economics of extended pre-strip stripping', 13th Coal Operators’ Conference, University of Wollongong, The Australasian Institute of Mining and Metallurgy & Mine Managers Association of Australia, (2013), pp. 355 – 367.
  5. Price Waterhouse Cooper. Mining for efficiency. PWC. (August 2014).
  6. Price Waterhouse Cooper. Truck and Loader Dictionary, 2nd ed. (2013).
  7. Motion Metrics International Corp. ShovelMetrics Payload Monitoring Report, Truck Load Analysis. (January 2015).
  8. Motion Metrics International Corp. ShovelMetrics Payload Monitoring Report, Truck Load Analysis. (January 2015).
  9. Motion Metrics International Corp. Internal Monthly Truck Load Analysis Reports. (January 2016 – August 2016).

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