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AI-based Volume or Throughput discharge model of Screens
Application
Application
Minealytics offers a range of neural networks to model screen discharge in a screenhouse that can understand and predict the behavior of screened ore fractions based on various inputs. Our neural networks, with their ability to learn complex patterns from data, can be particularly effective in environments with multiple complex cross-correlated variables, such as a screenhouses.
Our screen models use the following process variables to model discharge rates and split ratios:
1. Level Change Rate Above Screen: This serves as a primary indicator of the input flow to each screen. A higher rate of level change would typically indicate a higher flow rate of input material.
2. Cumulative Fractions: The total output from all screens, measured accumulatively, helps in understanding the overall performance and efficiency of the screening process.
3. Screen Current: The electrical current drawn by each screen can indicate its load; higher currents may suggest more material is being processed or difficulties in processing (possibly due to screen wear or the characteristics of the material).
4. Feeder Speed: This controls the rate at which ore is fed into the screens and can significantly affect the screening efficiency and the load on each screen.
Our neural model is trained using historical data from the screenhouse operation. During training, the network learns the complex relationships between the inputs (level change rate, screen current, feeder speed) and the desired outputs (individual screen flow rates and split ratios). The neural network, through its layers and interconnected neurons, learns the non-linear patterns and interactions between different variables. For example, it might learn how changes in feeder speed affect the screen's discharge rates in conjunction with the current load on the screens. Once trained, the neural network can predict the individual screen discharge rates and split ratios based on real-time data. This predictive capability allows for proactive adjustments to be made to optimise the screening process.
Further process or operator inputs can be incorporated into the Minealytics Screen Models natively or in the format of hybrid models. These inputs are usually:
- Screen Mesh Size and Condition: The size and condition of the screen mesh can affect the screening efficiency and the split ratio. Including data on mesh size and monitoring for wear can enhance model accuracy. Mesh size can either be inputed by the operator or it can be fed in the format of image data using cameras above the screens.
- Ore Characteristics: Properties such as moisture content, particle size distribution, and hardness can significantly affect screening performance. Incorporating real-time or periodic assessments of these properties can refine predictions. Image data often utilised to infer these parameters.
- Environmental Factors: Temperature and humidity can also affect screening efficiency that are also often added to the screen rate prediction models.
Benefits
Benefits
The Minealytics Screen Discharge Rate 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 load and performance 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 discharge 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.
Measure, predicted screenhouse discharge rate
Technology
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.
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