Editorial Feature

Digital Transformation: The Rapid Rise of Intelligent Automation in Mining Applications

There is a need to physically extract or handle mining equipment, but the industry is increasingly making use of intelligent automation in the form of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) to improve operations.

artificial intelligence, mining

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The Digital Mining Revolution

There are plenty of opportunities within the mining industry to automate core processes, making a digital transformation an attractive prospect.

Digital transformation applies to the technology and initiatives that reduce the time and cost spent on production while also increasing production efficiency, maximizing yield, and meeting regulatory requirements.

Such a transformation may involve modernizing or retrofitting existing mining plants and equipment, or re-engineering processes and installations to create a cohesive and profitable operation.

What is Intelligent Automation?

Intelligent automation (IA) refers to the integration of robotics with components from different emerging technologies, such as artificial intelligence, blockchain, and the Internet of Things (IoT).

These software robots function like virtual employees to carry out manual repetitive tasks. By combining robotics, intelligence, and autonomous systems, the mining industry is able to broaden the range of tasks and processes that can be automated, transforming the whole spectrum of emerging technologies.

Intelligent Automation Improves Efficiency

There are many opportunities to introduce intelligent automation into the mining industry; you could automate equipment, software, or communications, for example, to create a safer, more efficient, and streamlined workplace.

A good place to start is by automating manual, repetitive tasks that require no decision making, such as payment processing or invoicing using robotic processing automation (RPA) tools or software robotics.

RPA technology implements natural language processing capabilities, content analytics, cognitive capabilities, and process automation, and allows data to be handled across multiple applications, for example, by receiving emails containing an invoice, extracting that data, and then inputting it into a bookkeeping system.

In such instances, IA has been a game-changer; not only can it reduce costs, but it also streamlines the process, and improves accuracy, consistency, and quality. Automation can also provide extra security, especially where sensitive data and financial services are concerned.

Data Analytics in Mining Operations

Mining operations are reportedly 28% less productive than 10 years ago. Declining ore grades, a shortage of labor, and increasing regulatory compliance are to blame, but it means the industry needs to increase productivity and efficiency to remain profitable.

Implementing a digital transformation program can help by providing a broad overview of the state of a company’s business at a single moment. Again, RPA has a role to play. It can extract, upload, validate or format data, and perform calculations at an increased speed and accuracy compared to human operators.

RPA tools can also help with estimating and validating the size of ore reserves, aid production reporting and reconciliation, and maintenance planning.

Intelligent dashboards bring this information together to offer insight into what areas of the mine are performing, and where improvements might need to be made to optimize output, all while keeping an eye on what is happening across the entire processing timeline.

Intelligent Automation for Safety in the Mining Sector

The mining industry presents many physical risks to those directly involved in extraction; automation can reduce these risks with the use of sensing and computer technology, for example, to create a safer environment.

CSIRO in Australia has come up with a solution using automated systems and advanced processes to reduce the chances of workers being exposed to dangerous processes, environments, and materials. They use sensors and real-time processing in a remote guidance system for longwall mining that steers equipment by plotting their position in three dimensions.

Such technology helps miners dig deeper and reach more remote locations to access the depleting resources required, but removes them from such hazardous situations.

Furthermore, this automated control allows operators to run the equipment remotely - the systems can be monitored from anywhere in the world via the internet – thus removing them from potentially hazardous situations, while additionally boosting the overall efficiency of the operation.

Automated equipment also has a part to play in improving conditions for ground-based workers. Electrified and autonomous drill rigs, scoops and trucks can mine and transport ore from the mine to the surface, while haulage trucks can continue the journey towards smashing, milling, and refining – all without the need for human interaction.

AI transport systems using GPS systems, wireless connectivity, and sensors, allow autonomous trucks to be driven on their own or by a remote operator. Computer systems provide data on the velocity and position of the vehicle to prevent accidents. Furthermore, productivity loss through breakdowns can be reduced, resulting in improved productivity and profitability.

The Future of Intelligent Automation in Mining

The mining industry is increasingly transforming its digital processes, and advances in IA have led to improvements in the automation of operations and a greater number of robotic devices within the mine locations.

IA offers the opportunity to optimize processes and maximize efficiency, but there are still barriers to overcome – it is not a one-size-fits-all solution and implementation will depend on individual companies and their mines.

Overcoming these barriers should be easy; it clearly improves end-to-end processes in a measurable and standardized manner and improves safety at the same time.

References and Further Reading

Raphael, S. and Narayanan, V. (2021) Intelligent Automation: The Secret Behind Sustainable Productivity In The Cement And Mining Industry ­– BloombergQuint [Online] https://www.bloombergquint.com/bq-brand-studio/intelligent-automation-the-secret-behind-sustainable-productivity-in-the-cement-and-mining-industry. Accessed 11th October 2021.

EY.com, Intelligent Automation – EY.com [Online] https://www.ey.com/en_uk/intelligent-automation. Accessed 11th October 2021.

Conger, R. et al (2020) Inside a mining company’s AI transformation – McKinsey & Company [Online] https://www.mckinsey.com/industries/metals-and-mining/how-we-help-clients/inside-a-mining-companys-ai-transformation. Accessed 11th October 2021.

Panchpakesan, S. Why Robotic Process Automation Makes Sense in the Mining Industry – Infosys [Online] https://www.infosys.com/insights/ai-automation/robotic-process-automation.html. Accessed 11th October 2021.

Taylor-Smith, K. (2021) How electric mining equipment is leaving its mark on the industry – NSEnergy [Online] https://www.nsenergybusiness.com/features/mining-equipment-future-will-be-electrical/. Accessed 11th October 2021.

Smith, B. (2016) Automation for Mining in Remote Locations AZO Mining [Online] https://www.azomining.com/Article.aspx?ArticleID=1349. Accessed 11th October 2021.

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Kerry Taylor-Smith

Written by

Kerry Taylor-Smith

Kerry has been a freelance writer, editor, and proofreader since 2016, specializing in science and health-related subjects. She has a degree in Natural Sciences at the University of Bath and is based in the UK.

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