HIGH PROFILE
Accenture sees the value of a connected mine in digging deep into the wealth of data available across the entire value chain to provide integrated , end-to-end situational awareness and systemic management
Accenture ’ s Industry X
To get a detailed insight into Accenture ’ s capabilities in helping shape mining ’ s future in terms of advanced data insights and analytics , IM Editorial Director Paul Moore took some time to catch up with Constantino Seixas , an Accenture Managing Director and Latin America lead for Industry X Digital Manufacturing and Operations
The company sees the value of a connected mine in digging deep into the wealth of data available across the entire value chain to provide integrated , end-to-end situational awareness and systemic management , and it has been one of the leading companies helping mines progress from structuring through to implementing data-led transformation with AI front and centre .
Q Where does Accenture see the future of mineral processing in terms of the industry moving to greater use of AI and machine learning ? A Data analytics is essential to mining activities . Almost all mining companies implemented data historians more than a decade ago and accumulated huge quantities of data from all production processes , including those in the mines , concentration plants , smelters , refining units and logistics – including ports . However , very few companies are using this data to its full potential yet . Data is fundamental to understanding process behaviour , identifying operating patterns , detecting where the bottlenecks are , calculating variability , predicting quality and asset failures and for root cause analysis . Absolutely every unitary process in mining will benefit from data analytics , automation and artificial intelligence ( AI ) technologies . If you are drilling , you can capture much more data than before , including for example on the rock hardness , based on the drill torque , and rock composition , using new instruments that are able to characterise the ore . After blasting , the fragmentation can be analysed by image processing , using drones , or by measuring rock size distribution inside the shovel bucket . This improves possibilities in optimising fragmentation and selecting the ore that will be fed into the concentration plants ; that is part of the precision mining concept . Of course , all of that depends on determining the exact position of mining equipment , using a high-precision Geographical Positioning Systems ( HPGPS ), and having real time connectivity . Safety applications also depend on data analytics . Video analytics can detect the presence of operators in the plant red zones , workers positioned under a hanging load , and the level of fatigue of truck drivers or crane operators , to name a few examples . New sensors are used to monitor tailing dams and this data is analysed by real-time algorithms that can detect an alarm condition . The mining industry is using AI in building models to make inferences about the future state of a system , or to create diagnostics to find the root cause of an anomaly .
Q What processes do you go through with customers to help them achieve a transformation in their data analytics using AI ? A The areas in which AI can be applied are manifold . Accenture is using AI in production planning , fleet dispatching , process control , quality forecasting , instrumentation data
validation , creation of virtual sensors ( soft sensors ), asset failure prediction , multi-plant synchronisation and product-blending optimisation , to name but a few applications . In all cases , value realisation can be measured . Achieving a good result is the expected consequence . Sustaining the result is another story and depends on defining permanent optimisation programs . Accenture also deploys data-driven consulting in its day-by-day activities . When consultants are visiting a plant , they collect data that helps to understand if there are opportunities for improvement and invariably there are . Suppose the team is visiting a concentration plant and collecting flotation plant data . By studying the variability in flotation recovery and in fine copper production , it is possible to measure the productivity gap and to estimate the benefits to be achieved from addressing this .
In some cases , the journey begins as an ad hoc problem-solving project , with the client facing a challenge to understand a quality , production or maintenance problem and we then help them deploy data analytics to solve it . From this experience , the client learns about the power of data and understands that the data they already have is an asset to be explored . Accenture can then structure a data-led transformation roadmap , showing all possible applications of AI across several key dimensions : operations , maintenance , asset management , logistics , sales , procurement and HR , to name a few . Accenture and the client can then collaborate to prioritise
Constantino Seixas , Accenture Managing Director and Latin America lead for Industry X Digital Manufacturing and Operations
74 International Mining | FEBRUARY 2022