AI modeling for cyclone feed, over and underflow densities prediction in the mining industry, particularly in processes like mineral processing, involves using data-driven techniques to predict and control the density of the slurry in a cyclone. This approach utilises various process variables such as slurry flow rate, particle size distribution, pressure at the cyclone inlet and outlet, and cyclone dimensions. Often density is not measured online and only sampled data is available that would still allow for the training of Minealytics Density model to predict the density in the absence of online readings.

In traditional systems, pressure control and PID (Proportional-Integral-Derivative) based density control are commonly used. These systems rely on set points and manual adjustments to maintain the desired density. However, they often struggle with the variability and complex nature of the process, leading to suboptimal performance.

Minealytics utilises neural network models to model cyclone density by leveraging large datasets that encompass various process parameters like slurry flow rate, pressure, and particle size distribution. These neural networks, a form of deep learning, are trained on historical data to identify complex, non-linear patterns and relationships within the data. Once trained, the neural network can predict cyclone density with high accuracy, enabling proactive adjustments to the cyclone's operating parameters. This predictive capability is extremely beneficial for controlling cyclones as it allows for real-time optimization of the separation process. By predicting density fluctuations before they occur, Minealytics' neural network models enable the implementation of preemptive measures, thus ensuring more consistent cyclone performance, reducing wear and tear, and improving overall efficiency. This approach not only enhances the accuracy of cyclone density control but also contributes to a more efficient, automated, and data-driven operational process in mining operations.

Scrubber Load model explanation

Technology

Temporal Neural Networks (TNNs) are a class of neural network architectures designed specifically for handling time series data. They are tailored to capture temporal dependencies and patterns within sequential data, making them well-suited for time series prediction tasks.

Unlike traditional neural networks that assume independence among inputs, TNNs are built to recognize and learn from the sequential nature of time series data. They are adept at capturing both short-term and long-term dependencies in data, which is crucial

for accurate time series forecasting. TNNs can handle various types of time series data, including univariate (single variable) and multivariate (multiple variables) series.

Key Features of Temporal Neural Networks

TNNs often incorporate mechanisms to remember and utilize past information, which is essential for understanding time-dependent patterns. This memory can be implicit, as in the case of certain types of recurrent neural networks (RNNs), or explicit, as in memory-augmented neural networks. TNNs can account for time lags of varying lengths, allowing them to make predictions based on both recent and more distant past data. They can adapt to changes in the underlying patterns of the time series, which is important in dynamic environments where the data-generating process may evolve over time.

Minealytics TNN Architectures

Traditional RNNs are a foundational architecture for TNNs, designed to process sequences by maintaining a hidden state that carries information across time steps.

LSTMs, a type of RNN, are particularly effective for time series prediction. They can capture long-term dependencies while avoiding issues like vanishing or exploding gradients.

Similar to LSTMs, GRUs are a more streamlined variant that can also capture long-term dependencies but with a simpler structure.

TCNs use causal convolutions, enabling the network to look only at past and present data without peeking into the future, which is crucial for making predictions.

Attention-based models, like the Transformer, can weigh the importance of different time steps in the series, focusing on the most relevant parts of the sequence for making predictions.