Lower commodity prices, weaker demand, environmental regulations, lower ore grades and higher running costs are the main challenges in the mining industry. Adopting sustainable cost control measures and generating quality materials analysis data can improve efficiency and process stability.(1)
Optimization of the mining processing plant is mostly driven by the need to reduce energy consumption and increase margins. Reducing cash costs and increasing revenues can be accomplished with the extensive use of automation and robots in the field and the laboratory, to gain in productivity and remain competitive.(2)
Today, almost all major mining companies are benefiting from automation and saving costs thanks to fast data generation/monitoring, as well as improved accuracy. In 2015, for example, Rio Tinto announced they could save $200 million a year using robots and Big Data.(3)
Automation, and/or the use of automated machines, can collect samples then send ore or concentrate to the laboratory much faster than manually. This means that incoming ore or final product can be monitored more frequently.(1) Collected data is transferred to an intelligent system for systematic analysis and generation of relevant statistics. This data can then be used to identify and manage operation problems, improvements (desired or needed), productivity, and costs.
To increase the efficiency of a process mining operation, combining technologies of robust and reliable systems can support all steps of the mining process, from exploration to the analysis of final products.
One such example of combining automation with additional technologies is Yara’s Siilinjärvi mine in Finland. Yara, which has been in continuous operation since 1979, is continuously investing in guaranteeing that their operations are safe, reliable, and profitable. The Siilinjärvi mine is the only apatite mine in Western Europe, and its main product is apatite concentrate. Carbonatite deposit is mined in two large open pits and subsequently processed on-site to extract apatite from the host rock with tailings stored in a tailings pond. When they noticed that production recovery and quality of the apatite concentrate were falling below their target values, Yara consulted Malvern Panalytical for a solution.
It was important for Yara to receive accurate feedback on mined material so that inefficient mining of less useful rock was avoided. Additionally, to achieve an increased recovery rate and enhance their product quality, Yara had the need to precisely control their beneficiation process of apatite. This can be realized by frequent analyses of the concentrates and tailings, delivering fast results. The solution involved combining technologies of X-ray diffraction (XRD) and X-ray fluorescence (XRF), which can be automated and deliver quantitative elemental and mineralogical information in a relatively short time.
At the Yara mine, incoming samples from five control points in the production process are split into several portions for XRD, XRF, LOI (loss on ignition), and combustion analyses. Robots take care of the sample transport between various sample preparation stations (i.e. mill, press and bead maker) and the analytical instruments, allowing laboratory technicians to concentrate on other tasks.
In this completely automated solution, samples are delivered in continuous batches and processed automatically 24 hours per day, excluding periodic maintenance. The analysis time is one hour and 15 minutes, excluding LOI; with LOI, the typical analysis time is one hour and 50 minutes, including the sample taking.
As used in all processes throughout a mining operation, solutions involving automation and combined technologies can improve productivity and efficiency as compared to traditional methods. Robots and automation are fast, accurate, systematic, and financially predictable; they can reduce costs and manage capital, while solving many challenges.
(1) Tusseau, Eric. “2016 Mining Automation.” White paper. PANalytical B.V. November 2016.
(2) “Uranium miner joins Big Data revolution.” Richard Roberts, 04 March 2015. Mining Journal.
(3) “Rio saves $200m a year using robots, big data.” Frik Els | Sep. 10, 2015. Mining.com.