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Even more Reasons why Big Data Analytics are so Important for the Mining Industry

At the start of July, we here at Mining IQ compiled our top 9 reasons for why mining companies should be investing into big data analytics right now. The piece was very well received, but on reflection (and a couple of nudges from readers) I definitely overlooked a few critical areas in operations with best practice big data analytics and results integration can have a serious impact on productivity and/or efficiency levels.

  1. Mine sampling and reconciliation – go back a few years and mine reconciliation was considered purely the remit of the geologists; a report they had to compile every few months (depending on how often shareholder feedback was required) that was rarely looked at and used purely for the purposes of feedback, rather than a starting point for business process improvement. This has all started to shift in the last 2 – 3 years, largely due to the last boom. The potential of great mine reconciliation to actually change processes in the plant, extraction or mine planning phases is starting to emerge. ne of the big hindrances behind reconciliation reports being used more by operations and production departments to improve their processes, has always been the vast quantities of data and information included in them. We’re now starting to see mine planners, engineers and the reconciliation geologists work more closely to extract valuable information that can help improve recovery levels and processing efficiencies.
  2. Pit to port – improving your supply chain from pit to port can obviously have huge impacts on profit figures, and it’s usually one of the top three areas mining companies admit severe deficiencies in. A lot of data surrounds the pit to port process (particularly focusing on rail for the purposes of this viewpoint), especially with the automation of any part of that process, such as the loading and stacking, but even without. Rio Tinto are particularly keen on using the data readily available at their fingertips to identify inefficiencies in the process, as noted in the latest issue of their M2M magazine where they’re shortly going to be trialling a method for precisely measuring the loading of train cars using a new algorithm that will allow operators to load more onto each car without running the risk of overloading.
  3. Process control and SCADA systems – The very nature of SCADA systems mean they hold a lot of data at their fingertips. The entire point of their existence is to acquire data and display it to the operator after all. In days when the volume of data being created in any given process, or facility, is
    ignificantly increasing, this is suddenly a lot more useful. Across the business the data they gather is being acknowledged as useful and relevant for strategic decision making right from a corporate level downwards. Using this asset data for making maintenance, scheduling, availability, or planning decisions is becoming a far more common occurrence.
  4. OH&S through ground monitoring and control – one of the best developments over the last decade has been that of automated ground control systems that can measure vibrations in the ground and send out a warning to miners either underground, or within the pit, that a roof collapse, or slope slide, is a very real possibility, so the area should be evacuated well in time. Obviously the nuances these systems can read can also mean it can just be a warning for reinforcements, or more careful drill and blasting procedures. It is yet another example though where improved data analytics and use can help save lives, and prevent dangerous incidents.
  5. Drill and blast operations – between drill pattern design, blast pattern design and then the actual drilling and placement of the explosives, data plays a huge role in ensuring the success of drill and blast. Everyone knows productivity really centres about this critical function, as if that’s not working right, no resource is being accessed! For the drill and blast engineers the challenge is often knowing which of the data pools provided by geology the critical ones are, rather than not having enough data to make informed decisions (a fairly typical challenge faced by many in big data). Understanding rock porosity, ground density, water levels, heat levels, exact resource location, seam make up etc. are the key factors D&B engineers should be provided with. There’s no need to provide them with every available piece of data!

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