Oversize Management & Control

Application

Oversize and rock-breaking events can be a major source of crushing downtime, but their contribution varies by site and operating practice. Oversize material is unavoidable, and its management is needed to process material at the safest and most effective point in the circuit. Large pieces of ore can form bridges, obstruct flow, increase equipment loading and require intervention. Often, oversize is also useful feedback for mine and blasting practice. Real-time monitoring can support earlier, better-informed responses while preserving the site’s existing control and safety boundaries.

In the field of mining, one of the most significant areas where object detection could have a substantial impact is in detecting and managing oversize. Minealytics offers a comprehensive end-to-end solution for Oversize Management and Control.

System Description

Oversize Detection

Minealytics uses object-detection models to estimate the size and location of oversize material in real time and expose it to the control system and operations dashboard. In harsh, dusty environments, such as above a jaw crusher, a specialised laser camera can augment the vision data where the site design requires it.

Prevention and Control

Mass flow-based control is used uniformly for primary control of product discharge flow in a crushing circuit. While a number of feed-forward strategies have been trialled (bin level, AF hydraulic system pressure, AF current, grizzly/crusher current ratio, crusher current, etc.), they have all suffered from the deficiencies of the underlying model. Minealytics can provide more actionable insight about the load conditions than any of the listed process signals, which can then enhance the existing apron feeder throughput model and its control scheme.

Minealytics can integrate with the following subsystems within the PCS to improve crusher utilisation:

• Operator:
  • Reduce the chance of missed bridges
  • Reduce time to respond to bridges
  • Remove load on operators by monitoring the camera feeds
  • Provide preventive feedback about the likelihood of bridge formation
• Mine / Blasting:
  • Oversize feedback to the mine for blasting optimisation
• Control System:
  • Vibrating grizzly oversize loading feedback control
  • Crusher blocked interlock to apron feeder
  • Enhanced apron feeder control scheme

Closed-loop control of the apron feed can be enhanced via the feedback of vibrating grizzly load, which would substantially alleviate the chance of packing and overloading bridges in the primary crusher.

Minealytics offers real-time machine-learning models that can predict the chance of a bridge forming based on the combination of visual and PCS data (e.g. crusher current, apron feeder hydraulic system pressure, ROM bin level, etc.).

Recovery

Minealytics offers an integrated, vendor-agnostic Autonomous Rock Breaking (ARB) algorithm that can support rock-breaker positioning above detected bridges. Any automatic initiation of rock-breaking is subject to the machine, site controls and approved safety case.

The Minealytics ARB has been evaluated in a purpose-built rock-breaker simulation environment. The environment models machine dynamics and obstacles, allowing positioning and collision behaviour to be tested before site deployment. Any live operating mode remains specific to the machine, environment, controls integration and site safety case.

Technology

Leveraging YOLO’s real-time object-detection capabilities, it is possible to develop a system that can rapidly and accurately identify instances of oversize in either primary feed or primary crushed ore. With its robust balance of speed and accuracy, YOLO can operate on conveyor belts or over crushers, hoppers, or overlooking truck trays and loader buckets. The algorithm can localise oversize in real time, allowing for prompt removal and reducing the likelihood of process downtime and downstream issues. Its versatility and adaptability mean our team can train the network to recognise oversize, making it a valuable tool for enhancing the efficiency and safety of mining operations.

The YOLO framework, noted for its effective balance of speed and accuracy, has seen multiple iterations, each building upon the previous ones to address limitations and enhance performance. These improvements span various aspects including network design, loss function modifications, anchor box adaptations, and input resolution scaling, with trade-offs between speed and accuracy marking the framework’s development.

DETRv2 benefits from being an end-to-end transformer-based model. It eliminates the need for anchor boxes and non-maximum suppression (NMS), simplifying the pipeline and reducing error sources. DETRv2 leverages transformers to encode global relationships between objects in the image, focusing on both local and global interactions simultaneously. With deformable attention mechanisms it can handle small objects and occlusions better, dynamically focusing on the most relevant parts of the feature maps. It generally handles larger images and resolutions better, and its transformer architecture is inherently more flexible and extendable for other tasks like segmentation and panoptic tasks.