A few years ago, digitalisation in mining and aggregates was discussed only in the pilot projects of large mining companies; today it is on the agenda of medium-sized plants too. Thanks to falling sensor and communication costs, wider mobile network coverage and cloud-based platforms, the energy consumption, crusher settings and failure risks of a crushing and screening plant can now be monitored from an office, or even from a phone.
In this article we look at the layers that make up automation in crushing and screening plants, what remote monitoring and the digital twin concept bring, and how you can digitalise your plant step by step.
The Layers of Automation
1. Basic control: sequential starting and interlocks
The foundation of every plant: conveyors and machines start in sequence from the end back to the beginning, and when a machine stops, the machines feeding it stop too. Emergency stop, misalignment and slip switches belong to this layer. Without this foundation, optimisation in the upper layers cannot work safely.
2. Machine-level automation
- Automatic feed control: The feeder speed is adjusted automatically by measuring the crusher’s power draw, hydraulic pressure or crushing chamber level. The crusher runs continuously full and at full load.
- Automatic CSS setting: In hydraulic cone crushers, setting systems that compensate for liner wear and keep product size constant.
- Overload protection: Automatic opening and return when uncrushable material enters.
- Lubrication and temperature control: Automatic management of heaters and coolers, alarms and interlocks.
3. Plant-level control (SCADA)
A system that brings together the status of all machines, power consumption, production data from belt scales and alarms on a single screen. The operator follows the plant flow on a graphic diagram; shift reports and downtime analyses are generated automatically.
4. The optimisation layer
Higher-level algorithms that consider several machines together; for example, balancing the load on the secondary and tertiary crushers to maximise the plant’s total capacity or the proportion of a particular product. Artificial intelligence and machine learning models sit in this layer; they learn from historical data and recommend or automatically apply optimum settings.
What Can Remote Monitoring Do?
- Live status: Which machine is running, which has stopped, and why?
- Performance indicators: Tonnes/hour, kWh/ton, availability, product distribution.
- Condition monitoring: Bearing temperatures, vibration, oil pressure; SMS or e-mail notifications when thresholds are exceeded. See our predictive maintenance guide.
- Wear tracking: Monitoring liner and screen media life by tonnage, so spare parts are ordered on time.
- Remote support: The service team can diagnose faults from the data without visiting the plant.
- Fleet management: Comparative monitoring of several plants or mobile units from a single dashboard.
What Is a Digital Twin?
A digital twin is a virtual model of a physical plant or machine, fed with real-time or up-to-date data. In crushing and screening plants, digital twins are used to:
- Simulate the process: See how the plant will behave with different crusher settings, screen apertures or feed properties before trying them on site.
- Plan capacity: Calculate in advance the effect on the plant of a new product demand or material from a different area of the quarry.
- Predict wear: Model how liner profiles change over time to forecast replacement timing and crushing chamber performance.
- Train: Train operators on a simulation without putting the real plant at risk.
A digital twin is only as good as the data it is fed with. Without accurate belt scales, regular sieve analyses and reliable sensor data, a digital twin can produce misleading results.
Image Processing and Artificial Intelligence
- Particle size analysis: Real-time estimation of the size distribution of material on a conveyor from camera images; automatic correction of crusher settings.
- Foreign object detection: Detecting bucket teeth, large metal pieces or oversize blocks before they enter the crusher.
- Belt damage detection: Early detection of rips and damage to the conveyor belt.
- Anomaly detection: Automatic detection of deviations from normal operating patterns and notification of the operator.
A Step-by-Step Digitalisation Roadmap
- Strengthen the foundations: Sequential starting, interlocks, safety switches and a reliable electrical infrastructure.
- Measure: Belt scales, energy meters, crusher power and pressure data.
- Visualise: Use SCADA or a cloud dashboard to make the data understandable to everyone.
- Machine automation: Automatic feed and setting control on crushers — usually the step with the fastest payback.
- Condition monitoring: Temperature and vibration sensors on critical machines, an oil analysis programme.
- Optimisation and digital twin: Plant-level optimisation and simulation once enough data has accumulated.
Cybersecurity and Data Ownership
Remote access brings cybersecurity risks with it. Separating the control network from the office network, secure VPN connections, strong authentication and regular updates are basic measures. In addition, ownership of and access rights to data collected on equipment suppliers’ cloud platforms should be clarified in contracts.
Frequently Asked Questions
Can an existing old plant be automated?
Yes. Even in old plants, automatic feed control and remote monitoring can be set up by adding sensors, PLCs and frequency inverters. On crushers without hydraulic adjustment, setting automation is limited, but feed control alone brings significant gains.
Does automation eliminate the need for operators?
No; it changes the operator’s role. Instead of adjusting machines by hand, the operator takes on a role of monitoring the process, managing deviations and coordinating with the maintenance team.
Which step should I start digitalisation with?
Measurement. Any optimisation carried out without reliably collecting production and energy data is based on guesswork.
How Does Automatic Feed Control Work? An Example
A typical control loop set up to keep a cone crusher choke fed works like this:
- A radar sensor above the crusher measures the material level above the crushing chamber.
- The PLC adjusts the speed of the vibrating feeder under the surge bin via a frequency inverter to keep the level at the target value.
- If the crusher’s power approaches the upper limit, the feed is reduced regardless of the level target (power limitation).
- If hydraulic pressure peaks become frequent, the system warns the operator or opens the CSS slightly.
- If the screen or conveyor after the crusher is overloaded (current or belt scale signal), the feed is throttled back.
Once this loop is in place, the crusher runs full and balanced for long periods without operator intervention. The results most often seen in the field are more stable product distribution, fewer overload events and more even liner wear.
Which Data Should Be Collected?
| Data | Source | Use |
|---|---|---|
| Tonnes/hour | Belt scales | Production, availability, kWh/ton |
| Power (kW) and current | Motor drives, energy analysers | Crusher load, energy efficiency |
| Crusher CSS, hydraulic pressure | Crusher control system | Product size, wear tracking |
| Level | Radar/ultrasonic sensors | Feed control, bin management |
| Temperature, vibration | Condition monitoring sensors | Predictive maintenance |
| Reasons for stoppages | SCADA + operator input | Availability analysis |
| Laboratory results | Sieve analyses, quality tests | Linking product quality with process settings |
Key Performance Indicators (KPIs)
- Availability: The proportion of planned operating time during which the plant actually runs.
- Utilisation: How much of the plant’s capacity is used while it is running.
- OEE-style overall efficiency: Availability × performance × quality.
- kWh/ton and wear part cost/ton.
- Product distribution: The proportion of saleable fractions and their match with targets.
- MTBF / MTTR: Mean time between failures and mean time to repair.
Showing these indicators on screens by shift and day helps operators and maintenance teams focus on the same goals.
An Example from the Field: Making the Reasons for Stoppages Visible
A plant believed its capacity was insufficient and was planning to invest in a new crusher. A simple stoppage reason entry was added to the SCADA system: at every stoppage, the operator chose a reason from a list. Within a few weeks, it became clear that a significant share of total downtime was caused by a blocking transfer chute and waiting for trucks. The chute was redesigned, truck traffic was reorganised, and monthly production increased markedly without the need for a crusher investment. The biggest benefit of digitalisation often comes not from complex algorithms but from making invisible losses visible.
More Questions
What can be done at sites with a weak internet connection for remote monitoring?
Data can be collected and stored locally on site and sent to the cloud when a connection is available. If the mobile network is insufficient, a satellite link or directional antenna solutions can be considered.
Will artificial intelligence run the crusher entirely by itself?
Today, AI solutions are mostly used as support systems that make recommendations to the operator or adjust settings within defined limits. Safety interlocks and operator supervision must always be maintained.
Can machines from different brands be monitored in one system?
As long as standard communication protocols (such as Modbus, Profinet, OPC UA) are supported, equipment from different brands can be combined in a single SCADA or monitoring platform.
A Smart Plant with CSP Mühendislik
CSP Mühendislik integrates automation at the design stage of new plants and offers feed control, energy monitoring and crusher optimisation solutions for existing plants. Contact us to draw up your plant’s digitalisation roadmap together.
