start of the millennium. T is is not a regionally concentrated trend. Even the advanced mining regions of Australia and North America have shown a considerable productivity decline. In fact, the situation in Australia is particularly dire. According to a report from PwC, the productivity of mining equipment in Australia has underperformed all of its international competitors except for Africa.

The solution: getting the most out of every truck Mines looking to maximise cost savings should focus on the highest cost units fi rst. Haul trucks have seen the steepest productivity declines among all types of equipment. Despite their declining productivity, haul truck fl eets are becoming more important. With global strip ratios increasing by 4% annually and geological conditions becoming ever more complex, the mining industry is unfortunately burdened with an increased dependence upon pre-strip fl eets. Improving the cost- eff ectiveness of pre-strip truck and shovel fl eets is more critical than ever. Pre-strip operations face two major challenges: maximising shovel productivity; and matching that rate to the truck fl eet. Shovel operators are often better at the former than the latter. T e decline in truck productivity has outpaced that of shovels by a considerable rate. As there is generally a limit to the number of trucks an operation can aff ord, this leads operators to compensate by overfi lling trucks, improving productivity over the short-term, but increasing fuel costs and risking potential structural damage to the trucks over the long-term. Most manufacturers

specify that trucks may

In a test at an African mine, the payload monitoring system’s accuracy was shown to exceed that of on-board scales and was comparable to a weigh bridge

only be overloaded by 10%, up to 10% of the time, with no single load exceeding 20% of rated payload. According to PwC’s database, 93% of trucks violated this rule. In response, Motion Metrics has developed a sophisticated payload monitoring system for hydraulic shovels that can help improve payload compliance and maximise usage by avoiding damage, downtime or voiding the manufacturer’s warranty. Using robotics, estimation theory and sensor technologies, the ShovelMetrics payload monitoring system requires a limited number of sensors to maintain and has the benefi t of giving the operator immediate feedback on the shovel bucket before loading the truck, something that weigh bridges and truck-mounted systems cannot do. Knowing the payload prior to loading is the best method for getting the most out of each bucket and eliminating overloads; it is preventative rather than reactive. In a typical open pit mine, each shovel will load a fl eet of four to eight haul trucks. Hence, maintenance is more manageable on one shovel-based system than four or more truck-based systems.

How eff ective is the system? T e ShovelMetrics system was tested in a large African precious metals mine

on a Terex RH200 excavator and a fl eet of Terex MT4400 trucks with a rated capacity of 220 tonnes. T e payload monitoring system was compared to both onboard truck scales as well as a weigh bridge. Results showed that the system’s accuracy exceeded that of the on-board scales and was comparable to the weigh bridge. Once the system was put into use on the entire fl eet, the mine saw immediate operational benefi ts. Bucket fi ll improved by ~10%, reducing the total number of buckets to fi ll each truck. T e mine also saw improved payload compliance, reducing the number of overloaded trucks as well as the amount by which trucks were overloaded. Overloads greater than 110% rated capacity declined by 31% and the percentage of compliant loads (95%-110% of rated capacity) almost doubled, from 12% to 22%. Most importantly, these productivity improvements occurred with practically no change in total monthly production. T e mine was able to increase shovel productivity and reduce the costs of potential downtime and structural damage to its truck fl eet while maintaining its overall production rate. ●

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