Solution: Time Series Forecasting for Neural Control

Minelatyics' predictive control, powered by our neural model, can forecast pH changes based on current and historical data, allowing for preemptive adjustments before the pH moves outside the desired range. Neural networks excel at modeling complex, non-linear relationships, making them well-suited for predicting pH in the variable and dynamic environment of a SAG mill. A neural model can integrate a wide range of input variables, including feed characteristics, water chemistry, and operational parameters, providing a comprehensive view of the factors influencing pH. Neural models can continuously learn and adapt to new patterns in the data, improving their accuracy and reliability over time as they are exposed to more operational scenarios. By predicting the optimal amount of lime or other reagents needed to maintain the desired pH, predictive control can minimise reagent consumption, reducing costs and environmental impact. Maintaining pH within a tight range enhances process stability, improving the efficiency of the milling process and the effectiveness of downstream processes like flotation. Our predictive control can lead to more consistent operations, reducing the need for manual interventions and allowing for smoother, more efficient mill operation.

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.

Utilising AI modeling for pH prediction provides a powerful tool for optimal lime dosing and pH control of slurry. The predicted pH from an AI model is realigned with the measured discharge pH through online training of the AI model. This real-time calibration helps in maintaining accuracy and responsiveness. Additionally, the AI can detect issues with the pH sensor by analysing patterns and inconsistencies in the data; for example, sudden, unexplained changes in pH readings, or readings that consistently diverge from model predictions without corresponding changes in operational conditions, might indicate sensor fouling, scaling, or malfunction. By continuously monitoring the sensor's performance against expected outcomes and known process dynamics, the AI system can flag anomalies that suggest sensor issues, prompting maintenance or recalibration to ensure reliable pH control.