Benefits

The Minealytics Screen Efficiency Neural Model can be used to continuously optimise the screening process by predicting the discharge rates and in closed-loop making realtime decisions and adjustments to the feeder speed.

By understanding the efficiency of each screen, maintenance can be scheduled proactively, reducing downtime.

The model can adapt to changes in ore characteristics or operational goals, providing a flexible tool for process optimisation.

By leveraging our neural networks for screen efficiency modeling, mining operations can achieve higher efficiency, better predict and manage the wear and maintenance of screens, and optimise the entire screening process based on real-time data and predictive insights.

Technology

Time series forecasting can play a significant role in solving the problem of apron feeder control by providing accurate predictions of material flow rates. By analysing historical data and using forecasting techniques, engineers can make informed decisions to optimise the operation of apron feeders, or the control can be automated based on the predictions. Since there are multiple sensors and other data sources, AI-based models can efficiently bring together this data and create accurate predictions. 

Our technology uses Convolutional Neural Networks (CNNs) to accurately predict material flow rates from several variables including current apron feeder speed and current flow rate. Depending on the equipment and setup, any number of variables can be added to help model the future flow rate. CNNs are traditionally known for their prowess in image and video recognition tasks. However, their ability to recognise patterns in spatial data makes them a powerful tool for time series prediction as well. CNNs can automatically learn relevant features from the raw time series data, which makes feature engineering a less tedious task. CNNs are generally robust to noise and outliers, thanks to their hierarchical feature learning. This makes them well-suited for feedrate estimation, where data is generally noisy due to high flow rate, vibration, equipment malfunction, sensor errors, or other operational issues. Convolutional layers can learn to recognise both local and global patterns in time series data, which is crucial when dealing with feedrate estimation where short-term fluctuations and long-term trends both play significant roles. 

In summary, CNN-based time series prediction for feedrate estimation can offer a robust, scalable, and accurate solution, well-aligned with your goals to transform mining into a more data-driven and autonomous industry.