THE LEADER VOLUME 19 • NUMBER 5
Nexgen SIMS success
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Nexgen SIMS is a collaborative project run by a consortium of partners coordinated by Epiroc and partly funded by the European Union . It will be completed in April 2024 and aims to develop technologies , methods , and processes for the sustainable mines of the future .
The partners within Nexgen SIMS say they have developed and recently demonstrated a working solution for the work package : autonomous material handling at the face . The demo occurred at a depth of 750 m in Agnico Eagle ’ s Kittilä gold mine in Finland . It encompassed a fully autonomous cycle of material handling using battery-electric Epiroc underground loaders and mine trucks – evaluating the stope , filling the bucket , moving to a mining truck , and depositing the ore into the box , all without human intervention .
“ The demo went perfectly well , meaning that we have demonstrated a viable autonomous solution for this process . In short , we have increased the TRL – technology readiness level – for autonomous mining ,” says Vladimir Sysoev , Global Product Manager Deep Automation – Trucks , at Epiroc . The underground loader has LiDAR-based 3D and weight sensors and a processing unit with algorithms to control detection , manoeuvering and connectivity . The solution is integrated with 5G capability and depends on the site ’ s infrastructure for connectivity .
“ These solutions wouldn ’ t have been possible just a few years ago . That ’ s also a result of the project , that we have properly tested 5G in an actual mining environment . Everything went according to the plan . It ’ s been interesting to see how both the predecessor LTE and 5G have developed over time , and they have proved to be very suitable for these applications . The 5G standard is excellent for automation and Big Data solutions , and it is also future proof with ample room for more capacity and reduced latency ,” says Sysoev .
The demo contains three autonomous processes that are entirely novel to the global mining industry : evaluating the stope to decide where to dig , filling the bucket with the right amount of ore , and dumping the ore in the mine truck box while minimising spillage . The cycle of autonomous material handling at the face is complete in combination with autonomous tramming and dumping to and from the mine truck . An experienced operator can quickly scan the muck pile and decide where to start digging . The trick is to pick the right spot to excavate the pile evenly , working your way through it efficiently . In addition , stopes tend to be unstable , so there is always the risk of slides .
“ Until now , we have been reliant on the human eye to decide where to dig , even if the digging itself can be done semi-autonomously . But here , we have developed algorithms that can make decisions based on the scanned data and feedback from the resistance in the pile . It was hard , but we solved it ,” says Sysoev .
How do you know you have filled the bucket with the right amount of ore ? That might be relatively easy for a human but much harder for a machine . Scanning the bucket is not feasible since the front of the loader is situated in a very harsh environment at the stope . Nexgen SIMS has solved the problem by measuring the weight in the bucket , thereby determining the bucket fill factor .
The standard procedure is to match a loader with a dumper so that three bucket loads will fill the box . The goal is to distribute the load evenly in the box while minimising spillage . “ This was quite tricky to solve . We started by looking at how operators usually work – pick a good spot within the box , dump the load , then use the bucket to distribute the ore a bit if necessary . The algorithms we developed puts the first load in the front of the box , the second in the back , and then finally the third in the middle . The system can also use the bucket to press the ore a bit , which reflects the typical operator movements while dumping .”
Safety is a major driving force for developing autonomous solutions . Minimising human presence in hazardous areas in mines is imperative , and the solution ’ s safety is also paramount . “ The machines are smart enough to work independently , but for safety reasons they are constantly being monitored . There is a centralised server that orchestrates and manages the traffic in the mines . So , we need to know where the machines are , and how fast they are going , at all times . In addition , operators must have instant access to the machine in case anything goes wrong . For those reasons , the system is designed to immediately stop the machines in case communications goes down , even if it ’ s for less than a second ,” says Sysoev .
Another result of solutions like these is increased productivity . “ An experienced operator can on a good day probably move more ore , but one of the advantages of relying on algorithms is that there is consistency – you get the same result every time , which leads to better precision , planning possibilities and productivity in the long run . Consistency is one of the goals we have aimed to achieve .”
Nexgen SIMS is a consortium of 13 partners collaborating in an EU-sponsored project to develop autonomous carbon-neutral , sustainable mining solutions , building on the successful SIMS project . The partners are Epiroc Rock Drills , AFRY – ÅF Digital Solutions , Agnico Eagle Finland , Boliden Mineral , Ericsson , KGHM Cuprum , KGHM Polska Miedź , K + S Minerals and Agriculture , Luleå University of Technology ( LTU ), LTU Business , Mobilaris MCE , OZ Minerals and RWTH Aachen University . The project has had a budget of € 16 million and ran from May 2021 to April 2024 .
Paul Moore Editorial Director paul @ im-mining . com
MAY 2024 | International Mining 3