Adopting digital tools to manage mining tailings brings significant benefits in safety and sustainability performance.
As technologies continue to mature, they offer the promise of transforming how tailings facilities are monitored and managed, ultimately paving the way for safer mining operations and better protection of the environment and communities affected by mining activities.
Tailings dam failures have a catastrophic impact on the environment and surrounding communities, which is why industrialists and researchers are approaching the need for innovative solutions to enhance safety and disaster mitigation in tailings management with urgency.
In recent decades, the mining industry has grappled with numerous issues related to tailings dam failures, with up to five tailings dams failing globally each year.
The consequences are severe, leading to environmental degradation, loss of life, and significant economic losses.
Prominent examples, such as the 2019 Brumadinho iron ore mine tailings dam collapse in Brazil and the2022 disaster at the Jagersfontein diamond mine inSouth Africa, highlight the severe repercussions of inadequate monitoring and management.
Recent advancements in Industry 4.0 technologies have opened new avenues for developing digital tools that provide real-time monitoring of tailings dams.
Tailings dam stability analysis is essential for avoiding failure-related issues, with ongoing research and development of advanced monitoring tools needed to provide critical information.
Currently, Artificial intelligence (AI) and Internet of Things (IoT) technologies can process realtime data and recommend optimal courses of action, including early warning systems. This integration enhances predictive capabilities and automates responses to potential risks, ensuring operations comply with environmental regulations while safeguarding communities.
In research published in the Procedia ComputerScience journal last year, a data-driven framework for monitoring tailings dam stability was developed, with components such as real-time data collection, digital twin modelling, machine learning (ML)-based early detection, and intelligence-driven decision support.
The researchers said: “Current monitoring approaches have proven inadequate in the timely detection of early warning signs of failure.“As a result, cost-effective smart monitoring tools for tailings dam stability have become a critical need.
“Limited application of digital twin (DT) technology within the mining industry has emerged in the past few years. However, the concept has not yet been fully leveraged in tailings dam safety management, maintenance, and cost-effectiveness.”
The concept of this technology is the focus of the researchers’ proposed framework, which creates a virtual representation of physical assets. Simulating dam behaviour using real-time geophysical data collected by sensors, allows for the analysis of stability and failure predictions without requiring explicit mathematical definitions of the monitored parameters.
The report emphasises that “the proposed method does not suffer the limitation of requiring physical presence at the mining site,” making it possible to conduct remote monitoring efficiently. This capability is especially crucial in remote or hazardous mining locations where access may be limited.
The integration of ML into this framework enhances predictive capabilities significantly. Traditional monitoring methods often rely on periodic inspections and basic analysis, which can miss subtle changes in dam behaviour. In contrast, ML algorithms can process vast amounts of data continuously, identifying patterns and anomalies that indicate potential instability.
The necessity of these innovations is further corroborated by another report published in the Journal of Mining and Environment earlier this year, which noted that unplanned release of tailings and the increase in saturation due to rainfall events are major contributors to dam failures.
The study utilised numerical modelling alongside AI techniques to predict slope stability, demonstrating the effectiveness of ML algorithms like XGBoost, which gave the highest accuracy compared to other models.
This tool can provide valuable insights into the geotechnical parameters affecting dam stability, such as density, cohesion, friction angle, and saturation levels. Moreover, the study highlights the importance of integrating diverse data sources to create a comprehensive view of tailings operations.
AI can seamlessly integrate data from various monitoring systems, offering a holistic perspective that enhances decision-making. By presenting complex data in a user-friendly format through AI-empowered visualisation tools, stakeholders can quickly identify risks and take necessary actions.
Integrating AI in tailings management is not merely a technological enhancement but a pathway towards sustainable practices. AI systems can enhance real-time surveillance, with drones and sensors equipped with ML algorithms capable of detecting anomalies and potential risks.
These systems can facilitate early interventions, significantly reducing the risks associated with tailings storage facilities. The ability to act swiftly based on real-time data is crucial in preventing failures before they escalate into disasters.
The adoption of a digital framework for monitoring tailings dam stability offers the mining sector the means to develop a more accurate and comprehensive model of the tailings dam. While challenges remain, such as the need for robust data infrastructure and the integration of various technologies, the potential benefits are substantial.
The commitment to integrating cutting-edge technologies in tailings management is not just about improving operational efficiency; it is about safeguarding the environment and safety of communities.
