Maximizing the value of mining data, from smart sensing to operational intelligence

Many systems were initially designed to control operations rather than feed advanced analytics. For example, a temperature recorded every five minutes may be enough for operators. However, when you’re trying to detect the onset of a bearing defect, that frequency can mask critical behaviour. In many cases, simply adjusting acquisition frequencies can deliver substantial gains without any major investment.

On a related note, maintenance or operating plans aren’t often digitalized and are communicated to the people carrying out the work only verbally or on paper. Digitalizing these plans using computerized maintenance management system (CMMS) tools, scanning or voice recording and transcription solutions would turn field knowledge into usable operating data. 

Modern equipment often contains far more information than is used on a daily basis.

A variable-speed drive can provide:

• Load profiles
• Detailed energy data
• Advanced alarms
• Performance metrics
• Health indicators

For example, during a conveyor monitoring project, a company may discover it already has the data needed to assess mechanical overloads without installing a single new sensor.

The challenge is often data access, not its existence. 

More and more equipment manufacturers offer proprietary platforms for viewing the information associated with their equipment.

While these solutions deliver value, they can sometimes limit access to raw data.

This creates several challenges:

•  Vendor dependencies
• Recurring costs
• Integration difficulties
• Limitations in developing custom models
• Interoperability challenges

An effective digital strategy must ensure that data remains accessible and exportable over the long term. 

Smart sensing is no longer limited to adding physical sensors.

Today, it’s possible to create virtual sensors, also known as soft sensors.

A virtual sensor is simply a mathematical model, a simulation or an analysis that uses existing data to estimate variables that physical sensors don’t measure directly.

In the mining context, they can be used to estimate:

• Metallurgical recovery
• Ore quality
• Circuit performance
• Asset health
• Certain environmental factors

This approach often improves the informational quality of the data fed to an AI model. The model is then better able to reach a conclusion that leads to high-impact operational decisions.

Most mine sites already have an extensive camera network.

Traditionally used for security and monitoring, cameras can now become genuine measuring instruments thanks to computer vision.

For example, algorithms can:

• Estimate whether a milling system is properly calibrated
• Count truck loading cycles
• Measure shovel use and wear
• Detect unusual objects and blockages on a conveyor
• Estimate inventories
• Measure hopper fill levels

A camera that was used yesterday to monitor an operation can now provide quantitative data that feeds optimization and performance models. 

Even the best data loses value without adequate governance. Data governance means making sure the right people have access to the right data at the right time and that they can trust its quality.

The most advanced organizations clearly define:

• Data stewards
• Validation rules
• Quality standards
• Reference systems
• Change management processes

This discipline helps ensure the long-term viability of AI initiatives and prevents model performance from degrading gradually. 

Quality data that’s contextualized and governed properly is a source of value for any advanced analytics system. It can be used to optimize a metallurgical process, assess the health of a crusher to better plan maintenance activities or control flow rates to various retention ponds.

However, in most mining organizations, this information remains spread across several separate systems: control systems, data historians, maintenance platforms, planning tools, ERP systems and specialized applications. Each team tries to optimize its performance based on its own indicators and its limited visibility into operations.

Yet, optimizing a single area can produce unwanted effects elsewhere in the value chain. In such an interconnected environment, a local improvement doesn’t necessarily translate into a gain across operations as a whole. For example, it may create a bottleneck in hauling, crushing or ore processing, thereby cancelling out the expected benefits.

It’s generally as organizations gain digital maturity that they seek to implement a unified data platform capable of consolidating information from across their operations. This approach creates a single source of truth, which in turn provides a comprehensive and consistent view of operations.

However, the real value appears when advanced analytics and artificial intelligence tools put this unified information to work. Based on a shared understanding of operations, AI can generate different scenarios, assess their risks, simulate their impacts over time using digital twins and recommend the actions that deliver the best outcome for the organization.

The goal is no longer to understand what’s happening but to determine what to do next to optimize overall operational performance. 

The mining industry’s digital transformation doesn't start with an AI algorithm.

It starts with a better understanding and unification of the data already available in operations.

The companies that will be most successful with their AI initiatives are those that make full use of their data assets through a well-thought-out smart sensing strategy, while turning their existing infrastructure into a unified source of operational intelligence.

That’s when a major shift takes place: unified data becomes operational intelligence, allowing mining organizations to significantly improve their efficiency, agility and ability to make the right decisions at the right time.