Vehicle and Obstruction Detection

Implementing a computer vision-based system for controlling cameras in a mining pit area significantly enhances the safety and efficiency of autonomous vehicle operations. By leveraging advanced machine learning models and real-time processing, such a system can accurately detect and classify vehicles and hazards, ensuring that autonomous vehicles operate safely and effectively.

Oversize Detection

Minealytics utilises state-of-the-art object detection neural network models to accurately detect the size and location of oversize material real-time and feed it back to the control system as well as through a web dashboard to operations so blasting practices and crusher CSS settings can be optimised. In harsh, dusty environment (eg. above the jaw) specialised laser camera can be utilised to augment the vision data for processing.

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 then can 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:

1. Operator:

   • Reduce change of missed bridges

   • Reduce time to respond to bridges

   • Remove load of operators by monitoring the camera feeds

   • Provide preventive feedback about the chances of bridge formation

2. Mine / Blasting:

   • Oversize feedback to the mine for blasting optimisation

3. 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 puzzling 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.).

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.

Autonomous Rock Breaker Control

Minealytics offers a flexible and adaptable approach to controlling a rock breaker, capable of handling the large momentum of the machine, inaccurate sensor feedback, harsh operating conditions, wear and aging of components, changes in the environment, and variability in target bridge formations. By continuously learning from its interactions with the environment, RL can adapt its strategies to maintain optimal performance under a wide range of conditions.

Over Conveyor Detection

An over conveyor camera system features a high-speed global shutter machine vision camera, which captures sharp and clear images of fast-moving ore on the conveyor belt. This system is powered by an AI edge controller that processes images in real-time, detecting ore properties, contamination, anomalies, conveyor wear, and damage. The integration of fiber optic network switches ensures rapid data transfer and low latency communication between the camera, edge controller, and the central monitoring system. This setup allows for continuous, high-precision monitoring and analysis, enabling timely interventions to maintain conveyor efficiency and prevent operational disruptions.