PSD control through integrated screening and crushing methods

Accurate particle size distribution (PSD) in crushing and screening is critical for product quality and downstream efficiency. By incorporating sophisticated technology, real-time automation, machine learning, and sustainable practices, operations can improve performance, minimise resource consumption, and react to feedstock variations while maintaining high productivity and consistent output.

Precise control:
Equipment selection and optimisation: Choosing the right crusher type—such as jaw, cone, impact, or roll—is crucial and should be based on the feed material and the desired target PSD. Similarly, the efficiency of screening can be enhanced by using multi-deck vibrating screens, high-frequency screens, or air classifiers to achieve better classification.

Advanced Process Control (APC) and automation: Automated Adjustments use PLC/DCS systems to dynamically adjust crusher settings (CSS, speed) and screen parameters (angle, amplitude).

Feed material characterisation and pre-processing: Ore sorting and pre-screening help improve efficiency by removing fines or oversized material before crushing. Controlling moisture is essential to drying or conditioning sticky materials, preventing screen blinding. Moreover, niche products such as Ashar Locker’s Poly Ripple Screens can help reduce blinding, especially on the bottom deck.

Crushing and Screening Parameters Optimisation:

The Closed-Side Setting (CSS) adjusts the crusher gap to produce finer or coarser output. Optimising screen aperture and deck configuration—such as mesh size and inclination—helps achieve the desired cut points. Maintaining consistent throughput ensures a steady feed rate, preventing surges that could disrupt the PSD.

Classification and recirculation strategies: Multi-stage screening involves scalping, intermediate, and final screening to achieve tighter size cuts. Air classification is employed for ultra-fine separation below 100 µm. Oversized material is recycled by returning it for re-crushing, helping to minimise waste.

Maintenance and wear management: Regular maintenance of liners and screens involves checking for wear on crusher liners and screen meshes to prevent PSD drift. Predictive maintenance relies on vibration analysis and wear sensors to schedule replacements proactively, ensuring consistent performance.

Precise PSD control requires a combination of proper equipment selection, automation, real-time monitoring, and process optimisation. Implementing closed-loop control with AI-driven adjustments ensures consistent product quality and maximises downstream efficiency.

Data analytics:
Data Collection and Preprocessing are essential steps for effective process control in crushing and screening operations. Critical sensor data includes readings from various equipment sensors, such as crusher power draw, closed-side setting (CSS), vibration, pressure, screen deck acceleration, and feed rate. These parameters are crucial for monitoring and optimising equipment performance.

In addition, feedstock properties must be accurately measured. Key characteristics include moisture content, material hardness (typically represented by the Bond Work Index), and mineral composition determined through techniques like XRF or XRD.

Wear indicators are also monitored to assess equipment condition over time. Liner thickness can be measured using ultrasonic sensors, while screen mesh wear is tracked through image analysis.

The data preprocessing phase ensures that the collected information is reliable and usable. This involves time-series alignment to synchronise sensor data with inherent process delays. Outlier removal is performed to Filter erroneous readings caused by issues such as blocked chutes or faulty sensors. Lastly, feature engineering is applied to derive meaningful insights, such as calculating rolling averages of crusher power consumption, modelling wear progression based on throughput tonnage, and enabling dynamic adjustments in response to variations in feedstock hardness.

Machine learning techniques for optimisation
Predictive Maintenance and Wear Compensation uses powerful machine learning approaches to optimise equipment performance and minimise unscheduled downtime.  Liner wear is predicted using regression models, which take into account characteristics like throughput, ore hardness, and working hours. These devices allow automatic modifications to the closed-side setting (CSS) to offset wear-induced PSD drift, resulting in constant product quality. Furthermore, anomaly detection technologies can detect aberrant crusher vibrations or early indicators of screen blinding, allowing for early intervention before problems arise.

Dynamic Process Optimisation aims to continuously improve the overall efficiency of crushing and screening operations. Reinforcement learning (RL) reduces deviations from acceptable PSD requirements while optimising energy consumption. Time-series forecasting systems, such as Long-Short-Term Memory (LSTM) networks and Transformer models, can foresee PSD shifts caused by feedstock variability, such as abrupt material hardness variations. This enables timely modifications to keep the process on track while avoiding performance degradation.

Feedstock-Adaptive Control integrates physics-based models with machine learning to provide a more flexible process control system. This technique, which combines population balance models (PBM) and machine learning, can replicate the behaviour of crushers and screens under various situations. This hybrid model allows for more precise forecasts and adjustments to handle variations in feedstock characteristics better, resulting in greater process optimisation.

Energy consumption:
Reducing resource consumption (energy and water) in crushing and screening operations while maintaining productivity requires a combination of process optimisation, advanced technologies, and sustainable practices. Below are actionable strategies:

Energy efficiency improvements:
Optimise crushing circuit design: Pre-screening (scalping) removes fines before the crushing process, preventing energy waste by avoiding the processing of already-sized material. Selective crushing can enhance energy efficiency by using High-Pressure Grinding Rolls (HPGR) instead of traditional crushers, offering up to 30 percent energy savings in tertiary crushing. Additionally, cascade feeding directs coarse material to jaw or cone crushers, while fines are directed to energy-efficient vertical shaft impactors (VSIs), optimising the overall crushing process.

Smart process control: Variable Frequency Drives (VFDs) regulate motor speeds in crushers, screens, and conveyors based on real-time load, helping to reduce idle power consumption. Additionally, AI-powered dynamic adjustments using PLCs and DCS systems enable the optimisation of parameters such as closed-side setting (CSS) and power draw in real-time, ensuring more efficient operation and energy usage.

Energy recovery systems: Regenerative braking systems capture kinetic energy during conveyor deceleration. Waste heat recovery techniques also utilise heat generated by the crusher and screen motors to dry moist feed, particularly in cold climates.

Water reduction strategies: Dry processing methods can improve efficiency where possible. Air classification can replace wet screens with high-frequency air classifiers, such as Sturtevant’s Air Classifier, to effectively separate fines. Additionally, dry screening techniques, such as banana screens or flip-flow screens, are ideal for handling sticky or damp materials.

Water recycling and minimal use: Closed-loop water systems recycle water from slurry using thickeners and filter presses, such as Outotec’s Paste Thickener, to maximise water reuse. Additionally, dust suppression optimisation is achieved through ultrasonic fogging nozzles, which reduce water consumption by 50–70 percent compared to traditional spray systems. Smart misting systems enhance efficiency by activating only when dust sensors detect airborne particles.

Moisture control in feedstock: Ore Pre-Drying uses solar drying beds or low-temperature thermal dryers for high-moisture ores.

Equipment and maintenance best practices: Low-wear liners, such as ceramic or chrome-alloy liners, extend the lifespan of crushers and minimise downtime. Predictive maintenance systems monitor key parameters like vibration, temperature, and power draw to prevent inefficient operation caused by wear. Additionally, AI-based wear prediction helps reduce unplanned stops by 20–30 percent.

Renewable energy integration: Solar and wind hybrid power systems supplement grid energy with renewable sources to power conveyors and auxiliary systems. Companies are developing battery-electric mobile crushers to provide zero-emission solutions.

Digital and AI-driven optimisation: Digital twin simulations are used to test energy and water-saving scenarios before implementation, with tools like ANSYS Rocky DEM for particle flow modelling helping to optimise processes. Additionally, real-time resource monitoring through sensors tracks metrics such as kWh per ton and water per ton, allowing for the identification of inefficiencies.

Strategies and equipment designs:
Crushing and screening systems must prioritise modularity, automation, and multi-functional designs to adapt to fluctuating production demands and market conditions (e.g., varying ore grades, product specifications, or throughput requirements).

Modular and mobile plant designs:
Modular and mobile plant designs offer significant advantages, including rapid deployment, easy reconfiguration, and the ability to relocate plants as needed. Track-mounted mobile crushers and screens provide the benefit of quick setup (within an hour) and can be relocated to new sites. They can operate as standalone units or integrate into existing fixed plants. Furthermore, electric or hybrid options, such as battery-powered crushers, help reduce fuel dependency. On the other hand, skid-mounted and containerised modules are ideal for small-footprint plants, particularly in remote locations or for temporary capacity boosts. These modules offer flexibility with plug-and-play connections for crushers, screens, and conveyors and can be easily expanded by adding components like a secondary crushing module.

Adjustable and multi-functional equipment:
These offer flexibility and efficiency in processing materials. Hybrid crushers that combine cone and impact functions provide the advantage of switching between high reduction (impact) and precise shaping (cone) based on the processing needs. Variable-speed and smart screens further enhance performance, with high-frequency screens that adjust amplitude and frequency for different cut points, while flip-flop screens are ideal for handling sticky or wet materials without clogging. Additionally, multi-stage closed-circuit systems enable dynamic recirculation, automatically rerouting oversized material for re-crushing and offering scalable throughput by allowing the addition or removal of screen decks or crushers to meet changing demand.

Automation and AI-driven flexibility:
It plays a crucial role in optimising crushing circuits. Self-optimising crushing circuits utilise AI-powered process control, which adjusts parameters such as CSS, feed rate, and screen angles in real time to accommodate variations in ore hardness. Additionally, digital twin simulations enable the virtual testing of different configurations before implementation. Furthermore, predictive market-driven adjustments allow for demand-based optimisation—if the market requires coarse aggregates, the system prioritises jaw crusher settings. Conversely, if fines are needed, such as pelletising, the system can switch to VSI crushers combined with air classifiers to meet the demand.

Quick-change and wear-adaptive components:
Quick-release liners and screen meshes help minimise downtime when transitioning between abrasive or friable ores, ensuring continuous production. Meanwhile, wear compensation technology, including automated CSS adjustment, maintains the desired PSD even as liners wear, ensuring consistent crushing performance and output quality.

Scalable feedstock handling:
It involves adaptive systems that optimise throughput and material management. Adaptive feed systems, such as smart hoppers with load sensors, automatically adjust the feed rate based on the crusher’s power draw. Additionally, ore sorting helps pre-concentrate high-grade ore, further enhancing throughput. Universal crushers that can efficiently handle various materials, including limestone, recycled concrete, and slag, support multi-feedstock capability, ensuring flexibility in processing diverse feedstocks.

Energy and cost-efficient scalability:  
This is achieved through innovative power and infrastructure solutions. Hybrid power options like diesel-electric hybrids help reduce fuel costs while maintaining mobility. A combination of grid and solar/wind power sources provides energy efficiency for semi-stationary modular plants. Additionally, shared infrastructure like cloud-based fleet management enables remote monitoring, optimises equipment allocation across multiple sites, improves overall operational efficiency, and reduces costs.

Cost of ownership:
Evaluating and lowering the total cost of ownership for crushing and screening equipment requires a strategic approach that considers initial investment, operational efficiency, maintenance strategies, and lifecycle costs.

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