MINING SOFTWARE
Breaking down the silos
No modern mine is short of data , but what these operations continually lack is an ability to fully leverage this data across processes . Dan Gleeson speaks to those in the mining software space to find out how far off the industry is from ‘ connecting the dots ’
The last commodity price downturn got miners
thinking hard about how they could improve productivity across their operations to lower their input costs and stay profitable as their revenues fell .
In addition to taking steps to cut discretionary spending on the likes of exploration , external contractors and growth projects , the majors also sought to increase their productivity by incorporating new technology .
The likes of BHP , Rio Tinto and Fortescue Metals spent big on autonomous trucks at their open-pit mining operations in the Pilbara of Western Australia .
A surge in labour costs from the early 2000s , a need for further operational consistency and improved safety across their operations , and expectations of operating cost cuts were cited for such a move .
On the surface , the introduction of these autonomous vehicles achieved all these goals , bringing costs down and improving productivity .
More and more miners noted these improvements and factored in autonomous haulage – both above and below ground – into greenfield / brownfield projects . Expectations are that adoption of automation will continue apace into the future .
The speed of adoption across the wider mine and processing site has not kept up with these robotic trucks .
For example , autonomous haul trucks are completing their cycles quicker than most manned fleets ever have using sophisticated sensors on the trucks and information processed on-board to navigate the optimal route to and from the excavators and dumping locations .
Yet , the excavators filling the truck bodies with material and the supporting equipment servicing the load and haul equipment have not been taken on this same automation journey . This is despite pretty much all this equipment having entered the ‘ Internet of Things ( IoT ) space ’ with the incorporation of sensor technology .
In some instances , this disconnect between the autonomous and manual on site is leading to trucks queuing up in front of the shovels for periods of 10 minutes or more awaiting their turn , as frustrated control room operators look on .
The operations employing autonomous equipment are aware of this – again , using sensors on the trucks , excavators , ore chutes , etc – but , in general , they have been unable to align their existing processes to stop such queues and create even greater efficiencies . ‘ Data silos ’ are partly to blame for this . The addition of cost-effective sensors , new hardware and software platforms has seen the generation of data explode , but this data is tending to remain in one place , according to Shaun Maloney ,
CEO of geoscience analysis , modelling and collaborative technologies company , Seequent .
“ There are more data silos now than there have ever been before and they are growing at an exponential rate ,” he said . “ That is because of the deployment of data generators in the form of sensors , autonomous vehicles and automated processes .”
Dis-integrated thinking
This is not to say there is no value coming from the
data aggregated in these silos .
The data , whether analysed on- or off-board , has
provided benefits across mining operations – from geology with digitalised core logging , to drill and blast with increased control and precision , to
“ RPMGlobal ’ s software not only integrates together , but with other industry solutions to ensure the exchange of real-time information across all areas of a mine site ,” Paul Beesley says
processing with ‘ smart ’ control tools .
Yet , these ‘ digital transformation ’ projects tend to be run at a divisional level , with analysis and actions ending at the core logging , blasting and concentration stages , respectively . The thinking has not been integrated . “ The head-long rush into digital adoption did not factor in the cause and effect , and investment in the changes of workflow ,” Maloney said . There are plenty of reasons for this . For starters , habits are hard to break on mine sites . Most IoT projects are configured around replicating existing manual processes in the most efficient way as opposed to disrupting the process for the biggest gains , recognising that change management is a tricky task .
Contractual arrangements with suppliers and service providers around the likes of plant throughput , drilling metres per month , mine scheduling , etc are difficult to get out of , meaning miners stick with the status quo as opposed to breaking agreements and facing disputes .
Divisional incentives also come into this conversation .
It can prove difficult , for instance , to ask a plant supervisor to change the settings of the concentrator for the benefit of those working downstream on the flotation circuit if it negatively affects concentrator throughput . Many of these plant personnel are incentivised based on tonne per month throughput metrics , which need to be adjusted for operationwide projects to gain traction .
And , of course , there has been the problem of interoperability throughout the sector meaning miners struggle to obtain , translate and transfer data from vendors ’ machines or software to the rest of the value chain . ‘ Data ownership ’ issues with OEMs and miners have proven too much to overcome in many well-intentioned integrated projects .
22 International Mining | FEBRUARY 2021