How Mining Technology is Advancing Industry Roles

While a vast and highly profitable industry, mining does face an increasing number of pressures, particularly around environmental, social, and climate issues. With this in mind, the industry understands technology's importance in its survival and prosperity. 

The Equinix Mining Technology Report 2021-22 revealed that mining companies invest heavily in connectivity, telecommunications, automation, and data technology. A survey carried out as part of the report found that 93% are increasing overall spending on technology, and 44% reported they are significantly increasing investment. 

With the industry making active steps to increase the use of technology in mining, it got us thinking about how certain positions have evolved due to technology and what the future looks like in these roles. We look into the following topics:

• The history of technology and mining. 
• Roles that are increasingly reliant on technology. 
• What types of technology are used within these roles? 
• The key responsibilities of each role. 

Understanding the increased use of technology in senior-level mining jobs will help prepare our candidates to take the next step in their careers.  A crucial starting point for this discussion is the history of the role of technology in the advancement of mining jobs.

A Brief History of Mining Technology 

The mining industry adapting to technology runs throughout the history of the sector. For example, if we take a step back into the early days of mining, shafts used to be dug out by hand or using stone-based tools, making the task highly laborious. Eventually, these basic tools were replaced by fire, which involved piling heaps of logs near the rock face and burning them until the rock became weak and fractured. 

Fast forward to the late Middle Ages, when miners began using explosives made from black powder to break up large rocks. In the mid-19th century, black powder was replaced by dynamite, which was much more effective.

The Industrial Revolution between 1750 and 1840 saw further advancements in mining equipment and explosives. Mechanical drills, powered by pistons, helped increase the effectiveness and capability of mining hard rock. In addition, electric mine cars, vehicles, and conveyors replaced hand-powered loading and hauling devices. Finally, gas and battery-powered lamps were replaced with oil-wick lamps and candles. 

A more recent technological advancement is the rise of autonomous vehicles replacing human-operated vehicles. These vehicles were introduced in 2008 when Rio Tinto Alcan began testing the world's first commercial autonomous mining haulage system in the Pilbara iron mine in Western Australia. Some critical advantages of the use of autonomous vehicles are as follows:

• They do not require humans to be present in dangerous areas and tasks. 
• Approximately 20% rise in productivity. 
• Reduction in human error. 
• Can work 24 hours a day, seven days a week. 

Recent figures around autonomous vehicles show that the technology continues to be embraced by the industry and will be a significant part of its future. For example, between May 2021 and May 2022, the number of autonomous haul trucks in operation worldwide rose from 769 to 1,068, an increase of 39%. This figure is expected to reach 1,800 by the end of 2025. 

In our recent blog, 'Evolving Mining Technology and the Growing Need to Attract Talent to the Mining Industry,' we discussed the latest technological developments in the mining industry, how they are helping it advance, and how this evolution is creating challenges around attracting talent.

The Mining Jobs Embracing Technology

As you can see from history, mining is not afraid of adapting to the times and using technology to its advantage. So next, let's look at three roles that have fully embraced technology and are helping drive the industry into the modern age. 

Mining Planning Engineer

Mine planning engineers are in high demand, mainly due to the pressures for mines to reduce their impact on the environment whilst remaining productive and profitable. 

Mining planning is the mixture of mine design and the organisation of mining activities. Mine design aims to develop a mine site that allows for reserve exploitation in an economical, safe, and environmentally responsible manner. The design will include the following:

• The site's unique characteristics (size of resources, type of land, local habitat, local communities etc.).
• The product's anticipated market.
• The profit expectations.

Mine scheduling is concerned with the organisation of operations and the correct assignment of people and equipment to ensure that production targets are met. 

Mining companies want to maximise profits while committing to keeping their sites safe and reducing environmental effects. However, many constraints that mining sites experience are not always known at any given time, and many can change unpredictably over time. The uncertainty around restrictions creates risk, creating complexities for mine operations. Mine planning identifies short-term and long-term risks and finds appropriate, safe, and cost-effective solutions to ensure the smooth running of the mine's operations over its life cycle. 

Next, we take a look at three key technologies which are vital to the short and long-term planning of a mine and are becoming a staple of an engineer's toolbox.  

Operational Intelligence - OI is used when operators and engineers want to digitise parts of their operations to gain real-life insights into all mining operations and initiate data-based decisions. An example of its use is the digitalisation of dynamic assets such as workers and vehicles to visualise their behaviour. OI software can collect this data and provide recommendations to help improve efficiency, safety, and productivity. 

Drones - Drones can perform various mining activities, from mapping exploration, surveying, ensuring safety, and increasing security. They have proven to be a significant success as they increase data collection and improve productivity and safety. 

Drones can quickly provide accurate and comprehensive information on mine conditions, site safety management, surveying and mapping, and stockpile management. 

They have been hugely advantageous to engineers, as they can collect data twenty times faster than traditional methods carried out by personnel on the ground. As a result, they have allowed engineers to make well-informed decisions sooner. In addition, drones' aerial photography and high-resolution imagery are far more advanced and accurate than traditional mine mapping and inspections. 

Mine Planning Software - This software provides mining companies with a quick, accurate, efficient, and cost-effective tool to manage their global business interests. Today, virtually every aspect of mining uses planning software. For example, the software can estimate the financial return of a mineral deposit, find solutions for land rehabilitation after mine closure, and manage the infrastructure needed for mineral and metal extraction. Furthermore, as mining projects need substantial investment, the main advantage of using the software is that it can lower costs for production and maintenance.

Mine planning engineers are very much at the forefront of the rise of technology in the industry, and these advancements will continue to support them in their day-to-day roles. Next, let’s look at their key responsibilities:

• Ensure that underground resources such as minerals and metals are extracted safely and efficiently. 
• Assess the feasibility and potential commercial benefit of new sites. 
• Understanding extraction risks. 
• Developing plans or models for possible mining sites.
• Managing budgets. 
• Training and supervising staff. 
• Using specialist computer applications to maximise production and planning.

Mine planning engineers are required to hold a degree in a subject such as mining engineering, geology, or civil engineering. Having an advanced degree is advantageous and necessary for some positions. Engineers are also expected to have crucial skills, including:

• Problem-solving and analytics skills. 
• Strong technical skills. 
• Managerial and Interpersonal skills. 
• Organised and efficient. 
• Strong team working skills. 

Machine Learning Engineer

Machine learning is a type of computer science, and artificial intelligence focused on using algorithms and data to emulate how humans learn. Machine learning is present in all aspects of our daily life, from chatbots to Netflix suggestions to how your social media feeds are predicted.  

Machine learning has also helped to revolutionise the mining industry, and engineers are leaders in driving its benefits. Here are five key advantages of machine learning to the industry:

1. Monitoring Environmental Effects - Technologies such as satellite imagery help predict how a mine will cause changes in habitats, vegetation, and erosion. In addition, tracing systems can observe the effect of mining activities on ecological parameters such as temperature, groundwater, and underground ventilation.
2. Creates Shift Schedules - Machine learning can generate performance data about the production rates of various mining activities. Shift schedules can be designed using this data to maximise mining performance safely. 
3. Accelerates Mining Exploration - Real-time data enables the acceleration of timelines for multiple mining stages and decision-making intelligence. In addition, remote sensing data is used in soil classification and rock-face identification. Finally, satellite imagery, geophysical maps, and aerial photography are used to foresee mineralisation and the locations of mineral and metal reserves. 
4. Health and Safety - Machine learning has helped reduce accidents and injuries at mine sites. Data is collected from incident reports, near-misses, root causes analysis, and equipment pre-ops. Furthermore, machine learning can create algorithms which predict potential failures which can impact production or can cause harm to employees. 
5. Autonomous Vehicles - These driverless vehicles rely on software, rather than humans, to carry out predictive tasks such as hauling materials and positioning drilling equipment. Furthermore, these vehicles use machine learning algorithms to improve their efficiency and decision-making. 

Machine learning engineers are responsible for creating these algorithms and programmes that allow machines to take action without the need for human direction. In addition, a vital part of the role is that engineers allow computers to have the ability to learn automatically and improve from experiences without the need for a person to programme them. 

The primary responsibilities of machine learning engineers are as followings: 

• Understanding computer science fundamentals, including algorithms, data structure, and computer architecture.
• Identifying issues that need resolving to make programmes more effective. 
• Be a leader in software design and engineering. 
• Communicate with stakeholders to analyse business issues, confirm requirements, and agree on the resolution needed. 
• Support engineering teams in implementing machine learning in a product or system. 
• The continuous research and implementation of best practices to improve the current machine learning infrastructure. 

Most employers will expect a machine learning engineer to hold a Master's degree or a PhD in a relevant discipline. These disciplines include engineering, mathematics, statistics, and computer science. Furthermore, the majority of employers will seek extensive experience in computer programming. 

Data Scientist

Data science is booming, with career opportunities in virtually all industries. Mining is no exception, and recent activity and investment in the profession have shown that the industry realises the importance of its role in its future. For example, in 2019, the Australian Government announced a $7.67m investment to fund two mining research centres. The then-Education Minister Dan Tehan commented: 

"These centres will help Australia's mining industry better use data to make evidence-based decisions that lead to more efficient operations," 

These research centres aimed to train the 'next generation' of data scientists and engineers in artificial intelligence, data analytics, and advanced sensor systems to increase the value of mining and the processing of resources. 

The use of data analytics has been growing in importance. The insights gained from data collection can identify new markets, increase productivity, save money, drive innovation, save time, solve problems, and improve health and safety. So let's take a look at a selection of mining activities in which data is helping to improve:

• Worker's Safety - This is the most positive outcome of data use. With the Internet of Things (IoT) and other sensors, mining companies can monitor underground conditions 24/7. This surveillance means they can act immediately in an emergency. 
• Seismic Methods - Seismic methods are increasingly being used in the industry. For example, they provide high-resolution images of geologic structures holding mineral deposits, help target mineral deposits at depth, and support designing deep mines. Their use helps reduce safety and environmental risks and the exploration's overall footprint. Furthermore, as it is transitory and temporary, it is the least intrusive and cost-effective method of understanding where mineral resources exist. 
• Geological Modelling - Geological modelling is a computerised representation of geochemical, geophysical, structural, and lithological data below and on the surface of the Earth. The benefits are that it dramatically speeds up the exploration process, supports decision-making, provides instant feedback, and saves the business money.

The increasing importance of data within mining means that roles such as data scientists will remain in high demand. So, let's look at some of the main responsibilities of a data scientist:

• Involved in consuming data to build algorithms and models to predict risk and save lives. 
• Supporting the data analytics team in designing data capture setups for exploration work or changes in processes in existing systems. 
• Perform exploratory data analysis, provide quick iterations to identify problems, and seek potential solutions. 
• The development of statistical models and machine learning algorithms to analyse data to help address an issue or advance the business. 

Data scientists are required to have a Bachelor's degree in fields such as computer science, statistics, engineering, or mathematics. An advanced degree is also a bonus. They are also required to have technical knowledge in areas including the following: 

• Exploratory data analysis.
• Machine learning modelling experience with big data. 
• Mathematical and statistical modelling. 
• Experience in software architecture and system design.  

As mining is a global industry, a career as a data scientist can take you worldwide, working in the mines in fascinating countries. Your role will be vital to the advancement of the mining industry well into the future. 

Want to be Part of the Future of Mining?

Mining is an industry which stretches back thousands of years, but year by year, it continues to grow, advance, and be a fundamental part of our way of life. The advancements in technology and its plans to become a more sustainable industry mean that it has an exciting future ahead of it, and you could be part of it.

As a specialist mining recruitment agency, we are passionate about placing senior-level talent in positions within the global industry. If you want more information about how we can support you in taking the next steps in your mining career, visit our dedicated mining, metals, and minerals page.