Rio Tinto has opened a data center in India to collect and analyze operational information from its global fleet of mobile equipment. A growing portion of that fleet is composed of autonomous haul trucks similar to the 930E FrontRunner unit shown here. (Photo: Rio Tinto)
Outside of perhaps taking advantage of favorable market trends and currency exchange rates, one of the few strategies available to producers for improving revenue from mineral commodities mined at existing operations is to gain higher fleet productivity while reducing operating costs.
In a tough financial environment, fleet operators are intent upon getting more work—and service life—out of their trucks, and OEMs are intent upon supplying components and trucks that meet those objectives.
As an example from the producer side, Rio Tinto recently announced it will begin mining big data at a new Analytics Excellence Center to enhance equipment productivity across its global operations. The facility, according to the company, will assess massive volumes of data captured by sensors on Rio Tinto’s fixed and mobile equipment and enable experts to predict and prevent engine breakdowns and other downtime events, significantly boosting productivity and safety.
Using predictive mathematics, machine learning and advanced modeling, data scientists in the Analytics Excellence Center, located in Pune, India, will work to identify problems before they occur. This analysis will reduce maintenance costs and production losses from unplanned breakdowns.
Rio Tinto’s group executive for technology and innovation, Greg Lilleyman, said, “The Analytics Excellence Center will allow us to extract maximum value from the data we are capturing around the performance of our equipment, making our operations more predictable, efficient and safer.
“This is a world-first for the mining industry and is all part of Rio Tinto’s relentless pursuit of productivity gains across our businesses.
“The center will help us predict the future through the use of advanced data analytic techniques to pinpoint with incredible accuracy the operating performance of our equipment. Our aim is to run more efficient, smarter and safer mining operations and provide greater shareholder returns.”
Rio Tinto has partnered with IGATE to develop the Analytics Excellence Center. It is the latest phase of Rio Tinto’s Mine of the Future program, which is dedicated to finding advanced ways of improving safety and productivity. IGATE Corp. is a New Jersey, USA-headquartered integrated technology and operations solutions provider, reporting revenues of more than $ 1.27 billion and a global workforce of more than 33,000.
Optimization by Simulation
Meanwhile, technology from Siemens PLM Software LMS Virtual.Lab is helping mobile equipment builder Liebherr to explore design alternatives to improve its mine truck product line, by running complete vehicle simulations. This approach has allowed Liebherr to increase the payload of its TC282 C mining truck while reducing the time and effort needed for design optimization.
Instead of relying on physical models, Liebherr took a different approach to the optimization process: Using LMS Virtual.Lab simulation software, it was able to combine all the designs of the individual groups of engineers working on the project to create a single cohesive overall model. Siemens PLM Software generated a dynamic multibody simulation model, which integrated the mining truck’s components and units as multibody elements complete with their geometric and material properties. Any valid design had to allow efficient truck operation under even the toughest of application conditions and in different types of terrain.
LMS Virtual.Lab reconstructed the mining truck’s behavior under different loads, allowing the engineers to estimate the efficiency of the truck components and identify potential problem areas. The simulations were evaluated using finite element analysis. On the basis of the test results, the engineers were able to implement improvements to truck components and their mechanical design in the model. Following the first simulation runs, multibody loads for the final design were imported into the LMS Virtual.Lab durability software, with a view to also determining the fatigue life of crucial truck components.
Outlining the benefits of the software, Dr. Vladimir Pokras, who heads Liebherr Group’s Mining Truck Analysis and Simulation Division, said, “We were able to find alternative designs, which would have been highly laborious to determine using physical models. The ability to quickly integrate additional details into the model means that we can generate multibody models more quickly and with less risk of error than if we had to start from scratch again every time.”
Big Trucks, Big Fleets…Big Data
As reported recently in E&MJ (See Operating Strategies, May 2015), Caterpillar also announced a technology and predictive analytics agreement with Uptake, provider of a dynamic analytics and insight platform for a wide array of industries, based in Chicago, Illinois, USA. Caterpillar has invested in Uptake and will jointly develop an end-to-end platform for predictive diagnostics to help Caterpillar customers monitor and optimize their fleets more effectively. The new technology will be available for both Cat products and non-Cat branded products.
Komatsu announced a partnership with General Electric (GE) to provide big-data analysis services for mining customers, using Internet of Things (IoT) technology to boost efficiency in mining operations.
Komatsu also reported that its Autonomous Haulage System (AHS) recently passed a landmark milestone of 330 million metric tons (mt) of material moved.
Technology from Siemens allows Liebherr to efficiently explore design alternatives aimed at advancing its mine truck product line by running complete vehicle simulations. (Photo: Siemens)
Komatsu’s FrontRunner system, installed on conventional mining haul trucks, enables the haulers to run completely autonomously. A full truck fleet can be monitored by a single controller located many miles away.
Komatsu Australia’s managing director, Sean Taylor, recently stated that Komatsu leads the way globally in the successful implementation of autonomous haulage systems in production mining. Years of research and development efforts have been invested by the company to ensure the safe, productive and reliable operation of autonomous trucks, both in Australia and around the world.
Currently used for hauling either overburden to waste dumps or ore to the crusher area or stockpiles, the FrontRunner trucks are typically loaded by conventionally operated manned loading tools such as shovels or front-end loaders. Each truck is equipped with a combination of vehicle controllers, precision GPS, an obstacle detection system (ODS) using radar and laser, and a wireless network system developed by Komatsu.
The AHS Central Control System uses a detailed map of the mine area, including haul roads, loading areas, dump areas, and refueling and maintenance areas to assign required routes to each truck. The loading tools are also fitted with high-precision GPS and an integrated touch-screen computer showing the location and direction of movement of all items of mobile plant within the FrontRunner fleet’s operational area.
The loading tool operator uses an onboard touch-screen computer to “spot” the approaching truck to the correct loading location, informing the truck when it can move into position to be loaded, and then move off to the dump area once it is loaded. The autonomous system is able to determine whether the material has to be dumped at fixed crusher plant locations for mined ore or the overburden waste dumps.
According to Taylor, safety has been a priority during the development of the FrontRunner system. FrontRunner-equipped trucks can detect nearby light mine vehicles and other mobile mine equipment on site and will slow down or stop completely if required.
FrontRunner also addresses the problem of fatigue, especially at night—one of the biggest safety issues with truck operation. According to Komatsu, mine workers report that they feel safer and less stressed with FrontRunner trucks operating around them because of their constant and predictable movements.
The FrontRunner system has also significantly changed the personnel requirements to operate and control the trucks. Taylor explained that a Komatsu 930E truck in a 24/7 operation typically requires up to a total of five operators to cover shift changes and FIFO work patterns. A FrontRunner truck, in comparison, requires just a single controller per shift to supervise the entire truck fleet.
However, autonomous truck operations require significantly higher skills and more people to maintain and keep the system going, including specialists in electronics, GPS and control systems.
Komatsu said the FrontRunner system offers more accurate component life prediction because the trucks are consistently driven at their optimum operating capabilities. Tire wear is also reduced because the trucks stay within optimum travel speed, acceleration, braking and steering parameters.
In addition to lower fuel consumption, the system ensures increased productivity and production by eliminating any need to stop for shift changes or breaks; allowing longer periods between service requirements; and minimizing unscheduled downtime.