Application of AI in Automatic Particle Size Analysis in Chilean Crushing

[edit] Introduction

The mining and aggregate industries in Chile have been early adopters of technologies that improve operational efficiency, productivity and product quality. One of the most significant developments is the application of artificial intelligence (AI) to automatic particle size analysis within crushing processes. By combining computer vision, image analysis and machine learning, these systems enable continuous monitoring of material size distribution without interrupting production. This allows operators to optimise crushing performance, reduce downtime and improve the consistency of the final product.

[edit] Importance of particle size analysis

Particle size distribution is a key parameter in crushing operations because it directly influences equipment performance, downstream processing efficiency and the quality of finished aggregate products. Excessive quantities of oversized material can reduce crusher throughput, increase energy consumption, overload screens and conveyors, and contribute to blockages and unplanned maintenance.

Historically, particle size analysis has relied on manual sampling and laboratory sieve analysis. Although these methods remain important for calibration and verification, they are labour-intensive, provide only intermittent data and may not accurately represent the continuously changing material stream within a high-capacity crushing plant.

Real-time particle size measurement enables operators to identify process deviations quickly and adjust crusher settings, screen configurations and feed rates before production quality is affected.

[edit] AI-based particle size analysis

Modern AI-based particle size analysis systems typically use industrial cameras positioned above conveyor belts or transfer points. Images of the material stream are processed using computer vision algorithms that identify individual particles and estimate their dimensions and size distribution.

Recent advances in deep learning have significantly improved the ability of these systems to distinguish overlapping particles, irregular particle shapes and partially obscured material. AI models can also compensate for challenging operating conditions including dust, variable lighting and changing moisture content, although good camera positioning and maintenance remain essential for reliable performance.

Unlike traditional image processing techniques that rely on manually defined rules, machine learning models can be trained using large datasets to improve particle recognition accuracy across a wide range of rock types and operating conditions.

[edit] Operational benefits

Continuous AI-based particle size analysis provides several operational advantages:

• Enables real-time optimisation of crusher closed-side settings and screen performance.
• Improves product consistency by maintaining the required particle size distribution.
• Reduces unnecessary energy consumption through more efficient operation.
• Provides early warning of abnormal operating conditions, such as worn crusher liners, damaged screens or changing feed characteristics.
• Supports predictive maintenance by identifying gradual changes in process performance.
• Reduces the need for frequent manual sampling while improving process visibility.

The continuous flow of measurement data can also be integrated into plant automation systems, allowing operators to monitor trends, generate performance reports and support advanced process control strategies.

[edit] Applications in Chilean mining and aggregates

Chile's large-scale copper mining sector and extensive aggregate production industry provide favourable conditions for the adoption of automated particle size analysis. High production volumes, demanding operating environments and the need to maximise equipment availability make continuous process monitoring particularly valuable.

Applications include:

• Primary, secondary and tertiary crushing circuits.
• Quarry aggregate production.
• Mineral processing plants.
• Recycling facilities processing construction and demolition waste.
• Production of aggregates for concrete, asphalt and infrastructure projects.

In recycled aggregate production, AI-assisted monitoring can help ensure that processed materials consistently satisfy specified grading requirements, improving the suitability of recycled aggregates for construction applications.

[edit] Research and development

Research has demonstrated that AI-based online particle size analysers can provide reliable measurements across a broad range of particle sizes when appropriately calibrated and validated. Several prototype systems have been successfully deployed within Chilean mining operations, demonstrating the feasibility of continuous, non-contact particle size monitoring under industrial operating conditions.

Ongoing research is focused on improving model robustness, reducing calibration requirements, increasing measurement accuracy for highly irregular particle shapes and integrating particle size analysis with other process monitoring technologies.

[edit] Future developments

AI is expected to become increasingly integrated with digital process control systems throughout the mining and aggregate industries. Future developments are likely to include greater use of predictive analytics, autonomous process optimisation and digital twins that combine real-time sensor data with process simulation.

As computing power continues to increase and machine learning models become more capable, automatic particle size analysis is expected to become a standard component of intelligent crushing plants, supporting improved efficiency, reduced operating costs and more consistent product quality.

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