Editorial Feature

Intelligent Early Warning Systems for Improved Metal Mining Safety

The history of mining is filled with examples of costly catastrophic safety failures leading to loss of life and equipment. The ability to detect problems in mines before they cause a disaster is key to providing a safer and more efficient working environment.

mining safety

Image Credit: King Ropes Access/Shutterstock.com

Traditional Mine Monitoring Methods and the Importance of Monitoring

To ensure a mine is operating normally, monitoring is performed by workers. Subsidence, roadway conditions, and the health of mine infrastructure have traditionally been carried out via conventional methods such as theodolites, convergence gauges, and roof settlement gauges.

However, these methods can be insufficient due to limitations in range, efficiency, data gathering, the ongoing monitoring of roadways and empty spaces labor intensity, and issues with quantitative observation.

Monitoring is vital for the safety of miners and to avoid damage to material assets. In the case of metal mining, the monitoring of this operation is vital to the operation of the entire mine. Areas of the mine where resources are being extracted are the most prone to accidents such as roof collapse. Blasting and the mechanical action of mining equipment can cause accidents if not monitored correctly.

The Advantage of Intelligent Early Warning Systems

Adopting a more intelligent warning system for mines detects problems before they become critical safety issues. Surface subsidence, water levels, infiltration lines, gas explosion risk, and ore deformation are all factors in mining that require monitoring on an ongoing basis.

Intelligent early warning systems have several advantages over traditional monitoring techniques, including:

  • Mitigating collision risks between vehicles
  • Minimizing false alarms
  • Providing early warnings about worker safety
  • Predicting rock changes and critical infrastructure failure
  • Saving operational costs
  • Increasing productivity

Mines are complex environments that have several unique safety factors. Therefore, early warning systems must be designed according to the specific needs of mine operators. An intelligent early warning system is invaluable for increasing the safety of workers, particularly in deep underground mining operations where rescue can be difficult.

How an Early Warning System Works

An early warning system (EWS) allows workers to take effective action to minimize risks from exposure to harmful situations. It takes advantage of many technologies and methods, including:

  • Advanced sensors
  • Predictive algorithms
  • Automatic monitoring systems
  • Distributed networking
  • Machine learning
  • Software that interprets field data to provide early warning and inform intervention strategies

An early warning system has many parts, including the selection and identification of key indicators within the mining environment, early warning methods, safety threshold determination, and alarm systems that alert workers to potential hazards and allow them to act accordingly.

Designing an Intelligent EWS for Improved Metal Mining Safety

A paper published online in 2021 has demonstrated a new intelligent EWS for improved metal mining safety in China.

The system uses high-precision advanced sensors and a distributed management information system to provide real-time monitoring of metal mining operations to provide data for risk detection and mitigation. The system implements a multi-level early warning and risk condition forecasting process, and the software is based on Python.

The database employed in the system stores basic safety information on personnel, equipment, and environmental conditions. Alongside this information, data on the results of previous safety evaluations and the reasons for safety hazards are stored and help the system predict the likelihood of safety issues. Safety evaluation methods such as event trees and accident trees are used to evaluate and analyze major hazards during operation such as blasting and roof falls.

The system is divided into early warnings of human behavior as well as object state and safety management programs. The human behavior EWS provides information for judgments on employee safety, and the system for object state and safety evaluates equipment and rock states. The safety management plan evaluates and implements overall management procedures for the system.

Monitoring and providing early warnings for equipment status is the most important part of the system’s accident-prevention capabilities. Modules include the following systems:

  • Drainage
  • Ventilation
  • Shaft hoisting systems
  • Mining operation

Different evaluation parameters are programmed into the system for each module. The human behavior early warning system includes modules for:

  • Safety training management
  • Basic information management
  • Working environment management

Early warning indicators such as rock mass influence, the influence of the stope’s exposed surface, and the impact of rock blasting are monitored by the system. These key indicators help to predict the instance of safety issues either early on or before they arise.

Accident occurrence rules are programmed into the system using Visual Basic language, and the three safety states (danger, warning, safety) are visually represented by three flashing colors: red, yellow, and green. These warnings can be investigated further by safety managers. Permissions are divided into user and super user levels, with varying levels of access and database management.

The system constantly monitors the entire mining operation using automatic equipment, sensors, communication technologies, and power supplies throughout the metal mining infrastructure.

Toward a Safer Future for Mining

Safety and cost are two key issues in mining.

To ensure proper risk management and prevent catastrophic accidents, particularly in deeper resource extraction, innovative early warning systems that analyze previous accident data and predict future operational issues are vital for the industry.

Loss of life, equipment, and money will all be reduced using next-generation technologies and analysis of risk factors in metal and resource mining.

References and Further Reading

Wei, D & Jianhong, C (2011) ISMSSE – Review of Early Warning System of Mine Safety in China [online] Procedia Engineering 26 pp. 2287-2293. www.sciencedirect.com. Available at: https://www.sciencedirect.com/science/article/pii/S1877705811052799

Li, Y (2021) Research on the Intelligent Early Warning System for Metal Mine Safety. IOP Conf. Ser: Earth Environ. Sci. 714 022026. IOP Science. Available at: https://iopscience.iop.org/article/10.1088/1755-1315/714/2/022026

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.

Reginald Davey

Written by

Reginald Davey

Reg Davey is a freelance copywriter and editor based in Nottingham in the United Kingdom. Writing for AZoNetwork represents the coming together of various interests and fields he has been interested and involved in over the years, including Microbiology, Biomedical Sciences, and Environmental Science.


Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Davey, Reginald. (2022, February 01). Intelligent Early Warning Systems for Improved Metal Mining Safety. AZoMining. Retrieved on April 23, 2024 from https://www.azomining.com/Article.aspx?ArticleID=1633.

  • MLA

    Davey, Reginald. "Intelligent Early Warning Systems for Improved Metal Mining Safety". AZoMining. 23 April 2024. <https://www.azomining.com/Article.aspx?ArticleID=1633>.

  • Chicago

    Davey, Reginald. "Intelligent Early Warning Systems for Improved Metal Mining Safety". AZoMining. https://www.azomining.com/Article.aspx?ArticleID=1633. (accessed April 23, 2024).

  • Harvard

    Davey, Reginald. 2022. Intelligent Early Warning Systems for Improved Metal Mining Safety. AZoMining, viewed 23 April 2024, https://www.azomining.com/Article.aspx?ArticleID=1633.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this article?

Leave your feedback
Your comment type

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.