Tag Archives: loading and hauling

MACA to become contract miner at Atlas Iron’s Corunna Downs mine

MACA is to carry out open-pit mining at the Corunna Downs iron ore project in the Pilbara of Western Australia following a contract award from owner Atlas Iron.

The contract follows an agreement between the two to upgrade an existing public road and develop an access road at Corunna Downs, announced earlier this year.

The project, some 33 km south of Marble Bar in the Pilbara, will see Atlas develop five open pits using conventional drill and blast, and load and haul methods. Some 23.3 Mt of iron ore will be mined above the water table over an approximate timeframe of six years, according to a filing with the Environmental Protection Authority.

MACA will carry out the drilling and blasting, and loading and hauling as part of the new pact, which is expected to generate around A$230 million ($159 million) in revenue for MACA over the 62-month term.

MACA says it has a long-standing working relationship with Atlas having previously provided services at the Pardoo, Mt Dove, Abydos and Wodgina iron ore operations. It is also currently providing crushing services for Atlas at its Mount Webber iron ore mine on top of the civil works at Corunna Downs.

The contractor’s total work in hand position now stands at A$2.2 billion, it said.

MACA CEO, Mike Sutton, said: “We are pleased to have been selected as the contract miner for Atlas building on our workload in the iron ore sector with an existing client. We look forward to being part of the successful development of this project.”

Automated mucking and loading accelerates at LaRonde Zone 5

Automation efforts at Agnico Eagle Mines’ LaRonde complex in Quebec, Canada, continued to accelerate in the March quarter of 2020, with the company adding an automated production drill for testing at the LaRonde mine.

The complex, which includes the underground LaRonde mine and the LaRonde Zone 5 underground operation, has been testing out autonomous mucking and loading equipment for over a year.

In its March quarter results – which saw the company report a quarterly net loss of $21.6 million despite group production growing to 411,366 oz (from 398,217 oz a year earlier) – the company said it was evaluating an expansion of the mining rate to 3,000 t/d (previous guidance of 2,800 t/d) at LaRonde Zone 5. This followed an increase in daily tonnage in the most recent quarter thanks to continued productivity improvements and successful automation implementation (autonomous mucking and hauling).

Agnico also said automated mucking and hauling had already exceeded the target of 15% of 2020 tonnage in the March quarter alone.

The company said: “In 2020, the company will continue to test and refine automated mining techniques at LZ5. The goal is to increase the tonnage mined remotely to greater than 15% of the total tonnes mined in 2020. During the first quarter of 2020, LZ5 achieved the goal of exceeding 15% of total tonnes mined remotely and achieved a new daily record with 2,200 t/d mined with the automated fleet.”

At the LaRonde mine, meanwhile, the company said it continues to test automated equipment at the operation, explaining that, during the March quarter of 2020, the company began testing an automated production drill.

Micromine to release AI solution for underground loading and hauling

New underground mining precision performance software, using machine learning to refine and enhance loading and haulage processes, is set to be launched by global mining software company, Micromine.

The solution is to be released in early 2019 as part of the company’s fleet management and mine control solution, Pitram.

Using the processes of computer vision and deep machine learning, on-board cameras are placed on loaders to track variables such as loading time, hauling time, dumping time and travelling empty time. The video feed is processed on the Pitram vehicle computer edge device. The extracted information is then transferred to Pitram servers for processing and analyses.

Micromine Chief Technology Officer, Ivan Zelina, said the solution considered the information gathered to pinpoint areas of potential improvement that could bolster machinery efficiency and safety.

“Pitram’s new offering takes loading and haulage automation in underground mines to a new level,” Zelina said.

“By capturing images and information via video cameras and analysing that information via comprehensive data models, mine managers can make adjustments to optimise performance and efficiency.

“It also provides underground mine managers with increased business knowledge, so they have more control over loading and hauling processes, and can make more informed decisions which, in turn, improves safety in underground mining environments.

“This can contribute significantly to the overall optimisation of underground mines, which we believe have a lot of room for improvement.”

Pitram is a fleet management and mine control solution that records, manages and processes minesite data in real-time.

Micromine trialled the new technology in Australia, Mongolia and Russia as part of a research and development pilot programme.

The initial concept was on the back of a trial project in partnership with the University of Western Australia. One of the master’s students from the university was subsequently employed by Micromine to help drive the company’s development of machine-learning projects across its global business.

“This advance is another demonstration of how Micromine is operating differently to other software providers by extending our products well beyond simple built-in machinery automation to artificial intelligence,” Zelina added.

“The ability for mining companies to increase their knowledge of mining processes through automated data collection and analysis is endless and this is just the start of the work Micromine is doing with our mining software solutions.

“We’re striving to help companies optimise their mining value chain and we believe enhancing one of the most fundamental and critical underground mining assets – loaders – is a great place to start.”