Lionel Buck & Bianca Visagie
9 December 2021
The recent mudslide at Implats has placed the spotlight on safety in mining once again. While mining companies have made great strides to improve their overall health and safety, material unwanted events still occur. It has become imperative to reiterate the importance of critical controls and the digital technologies that improve mining safety.
Digital Technologies that improve Mining Safety
In today’s digital era, digital technologies are not hard to come by. The mining industry has made several moves towards digitisation, however, uptake of digital technology has been slow. Mines serious about monitoring conditions to proactively prevent incidents need greater insight into the circumstances and drivers of those incidents. By adopting the following digital technologies, mines can take their safety to the next level.
Data analytics and intelligence
Advances in analytics, from machine learning to improved statistical techniques for integrating data, help turn large sets of data into insight about the probability of incidents. With a machine learning algorithm, data from mines can be used to build leading indicators for catastrophic events. Other mining tasks such as geological modelling, on-the-day scheduling, and predictive maintenance are increasingly in the domain of data analytics and intelligence.
Data and critical controls
Define and execute critical controls clearly in order to prevent fatal and catastrophic events from occurring. However, very few mines are looking at digitising critical controls. Through digitising critical controls, advanced data analytics can increase safety measures.
The right data can assist mines to analyse previous events and build predictive models. These predictive models can prevent incidents before they occur. However, for these predictions to be accurate, the right data needs to be aggregated and analysed.
Miners already produce huge amounts of sensor data. This data enables mines to obtain a more accurate and consistent picture of what is happening on site. For example, if you have SAP data to track hours worked by employees, and you have the data of Incidents recorded, you would be able to see that there is a relationship between when people are working too much overtime or in fatigue shifts and when incidents occur. That data can predict incidents, and proper safety procedures can be put into place. Embedding vast numbers of sensors in equipment and machinery will increase the available data and increase the accuracy of predictions.
Wearables are not only used in the medical field but can be used in mining as well. Work clothing can now incorporate sensors that transmit data to managers about hazardous conditions and the physical condition of the workers themselves, improving safety outcomes.
Safety Analytics in Mining
Safety analytic solutions and platforms can assist operation managers and site supervisors in preventing unwanted safety events by providing them with predictive indicators based on historical data insights. The integration of various data sources, for example, safety (incident reports, safety officer inspection, planned task observations, critical controls, high-risk work verifications, high potential hazards, high potential intendants, visual felt leaderships), ERP system (downtime and work orders, shift data), production data (tonnes produced), maintenance tactics (planned maintenance and unplanned events) to facilitate artificial intelligence to identify patterns, risks, and opportunities. Examples of analysing and integrating various data sources to provide leading incident insights:
Example 1: Unplanned maintenance or breakdowns relates to higher incident and near misses
Data sources: ERP system and safety data sources
Conclusion: There is a higher chance of an incident to occur during unplanned working events. Thus, supervisors must be notified to ensure safety controls are in place and the alertness to workers must be conveyed.
Example 2: Higher amount of sick leave days/total workdays correlates to higher amount of incidents
Data sources: ERP system and safety data sources
Conclusion: There is a higher chance of an incident occuring during the event of someone acting in a trained supervisor’s role. Flag the area of the acting supervisor as high risk and ensure that more controls are put in place.
Example 3: Higher amount production throughputs pushed by operations with no stops correlates to a higher number of near misses and critical control violations
Data sources: Production data and safety data sources
Conclusion: There is a higher chance of an incident occurring during the event of the plant pushing for higher production throughputs to make up for production losses. Workers may experience fatigue due to asset monitoring and inspections working overtime. Notify supervisors to ensure safety controls are in place and that the workers are alert.
Safety Analytics at Integrove
Taken together, these technologies can cause a fundamental shift in the way mining works. By making use of the data gathered during the mining process, mines can greatly improve their safety. However, most mines do not have the ability to aggregate multiple data streams and make sense of the data. Another issue is the lack of integration. Rather than providing a view of safety performance in alignment with other operations throughout mining, these solutions often remain disconnected.
At Integrove we can help you with all your data and integration solutions. We build bespoke dashboards that analyse recorded data. We assist with streamlining hypotheses to provide you with leading safety indicators. Similarly, we can assist with predicting failure in assets via historical events, and we can do the same with safety data. Let us help you take your mine safety to the next level.