Neural Crusher CSS Control

Particle Size Detection

Our camera-based bridge detection systems over crushers offer several advantages over radar-based systems. First and foremost, camera systems can provide high-resolution, real-time visual feedback, enabling more precise characterisation of material properties and behaviour. This is particularly useful for detecting the early stages of bridging, where granularity and texture can be important indicators. Additionally, modern computer vision algorithms can be trained to recognise various types of anomalies in the ore flow, providing more comprehensive monitoring capabilities.

In contrast, radar-based systems may struggle to differentiate between normal ore levels and potential bridging incidents, especially when the material characteristics are heterogeneous. They can also be affected by environmental conditions such as dust, humidity and temperature, requiring frequent calibration for optimal performance. Moreover, radar systems only provide distance or depth information, which may not be sufficient to analyse the nature or cause of a potential bridging scenario.

The flexibility and adaptability of camera-based systems make them highly compatible with data-driven, AI-based analytical tools. This is crucial for predictive maintenance and real-time decision-making, helping to transform mining operations into smarter, more autonomous entities. With lower costs for advanced camera hardware and the increasing efficacy of computer vision algorithms, camera-based systems offer a more versatile and cost-effective solution for modern mining operations looking to optimise their crusher circuit.

Our model predicts the average energy use of the crusher within 10% accuracy only using image inputs. Further process data significantly increases the accuracy. This model forms a part of the Minealytics Dynamic Crusher Control Module that controles closed side settings and crusher speed. 

Neural Modeling of Ore Characteristics

The impact of ore hardness on the crushing process is a critical factor in mining operations, yet it poses a significant challenge in terms of measurement and modeling. Ore hardness directly influences the amount of energy required for effective crushing, the wear rate on crushing equipment, and the overall efficiency of the process. Traditional methods of estimating ore hardness through crusher parameters, such as electrical current draw, offer some insights but are often inadequate. These parameters can indicate changes in the crushing load, but the relationship between current draw and ore hardness is not straightforward and can be influenced by various other factors, making empirical modeling difficult and often unreliable.

This complexity has led to the exploration of advanced techniques like neural models for a more accurate assessment of ore hardness. Neural networks, with their ability to learn from large datasets and identify intricate patterns, can be trained on a range of data inputs, including crusher operation parameters, to develop a more precise understanding of the relationship between these variables and ore hardness. By continuously analysing operational data, these models can adapt to the subtle changes in ore characteristics, providing a dynamic and more accurate representation of ore hardness in real time. This capability is especially valuable in optimising the crushing process, as it allows for more informed and responsive adjustments to the crusher settings, improving energy efficiency, reducing equipment wear, and enhancing overall process control. In the realm of data-driven, AI-enhanced mining operations, the use of neural models for measuring ore hardness exemplifies the shift towards more sophisticated, adaptive, and efficient management of natural resources.

Neural Modeling of Ore Characteristics

Crusher load and work index depends on ore characteristics (particle size/density, hardness, abrasiveness) environmental factors like moisture content and crusher parameters (speed, shaft position, CSS etc). 

The Minealytics Neural Crusher model uses visual and time series inputs to determine the work index. 

Computer Vision

Visual data, when leveraged through advanced computer vision technologies, can effectively substitute for the traditionally used but often unreliable inputs of particle size, density, and moisture in the mining industry. Traditional methods of measuring these parameters can be fraught with inaccuracies due to manual sampling errors, equipment limitations, and variations in material properties. Computer vision, on the other hand, utilises high-resolution cameras and sophisticated image processing algorithms to continuously monitor and analyse the physical characteristics of the material in real-time.

For particle size determination, computer vision systems can accurately measure the dimensions of materials on a conveyor belt or within the crusher, providing a more consistent and comprehensive understanding than intermittent manual sampling or sieve analysis. This method allows for the immediate detection of size distribution changes, enabling quicker adjustments to the crushing process.

When it comes to assessing material density and moisture content, while direct measurement through visual data can be challenging, computer vision can infer these properties indirectly. By analysing the texture, color, and other visual characteristics of the material, and correlating these with known properties, the system can provide estimates of density and moisture levels. This approach, although indirect, can offer a more reliable and continuous monitoring solution compared to traditional methods, which may be invasive, time-consuming, or subject to environmental interference.

Incorporating visual data into the control systems of crushing operations thus presents a substantial advantage. It enables a more accurate, real-time analysis of the material being processed, leading to enhanced efficiency, optimisation of the crushing parameters, and ultimately, a more consistent and high-quality product output. This shift towards visual data analysis is a quintessential example of how data-driven, AI and computer vision technologies are revolutionising traditional practices in the mining industry.

The crusher parameters of speed and shaft position do not change in one tip so those are used along with the image input. 

The energy input is considered proportional to the new crack tip length created during particle breakage and equivalent to the work represented by the product minus the feed: 

Technology

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, crusher power draw, vibration and shaft position. 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.

Technology

By training a machine learning model on labeled images of typical and atypical ore conditions, Minealytics can achieve a high level of granularity in real-time monitoring. For example, the algorithm could be trained to recognise and differentiate between ore, empty space, and potential bridges. When the system detects a bridge forming, it can alert operators to take corrective action immediately, thus preventing potential jams, equipment damage, or inefficiencies.

The benefit here is twofold: not only does semantic segmentation allow for real-time, automated bridge detection, but it also offers the potential for predictive analytics. By continuously analyzing the characteristics and formation patterns of the ore flow, a well-trained model can predict the likelihood of a bridge forming before it even happens. This predictive capability can be invaluable in shifting from reactive to proactive management, enhancing operational efficiency and equipment longevity. Given these advantages, semantic segmentation aligns well with the aim of transforming mining operations into more data-driven, autonomous systems.

Neural Modeling of Ore Characteristics

The impact of ore hardness on the crushing process is a critical factor in mining operations, yet it poses a significant challenge in terms of measurement and modeling. Ore hardness directly influences the amount of energy required for effective crushing, the wear rate on crushing equipment, and the overall efficiency of the process. Traditional methods of estimating ore hardness through crusher parameters, such as electrical current draw, offer some insights but are often inadequate. These parameters can indicate changes in the crushing load, but the relationship between current draw and ore hardness is not straightforward and can be influenced by various other factors, making empirical modeling difficult and often unreliable.

This complexity has led to the exploration of advanced techniques like neural models for a more accurate assessment of ore hardness. Neural networks, with their ability to learn from large datasets and identify intricate patterns, can be trained on a range of data inputs, including crusher operation parameters, to develop a more precise understanding of the relationship between these variables and ore hardness. By continuously analysing operational data, these models can adapt to the subtle changes in ore characteristics, providing a dynamic and more accurate representation of ore hardness in real time. This capability is especially valuable in optimising the crushing process, as it allows for more informed and responsive adjustments to the crusher settings, improving energy efficiency, reducing equipment wear, and enhancing overall process control. In the realm of data-driven, AI-enhanced mining operations, the use of neural models for measuring ore hardness exemplifies the shift towards more sophisticated, adaptive, and efficient management of natural resources.