Digitalization in mineral processing connects plant measurements and other operational information to analytics, process models and control systems. It helps people see changing ore and equipment conditions, test possible settings, predict process behavior and, where engineered and commissioned for it, adjust selected variables automatically. The approach can cover crushing, grinding, flotation, thickening, filtration, dewatering and refining; it is a plant-wide operating method rather than simply installing a digital twin.
Digitalization, digitization and digital transformation are different
Digitization is the conversion of information into digital form, such as replacing a paper laboratory log with an electronic record. Digitalization uses connected digital information to improve a task or process—for example, combining online analyzer readings, laboratory assays and control-system tags to recommend a flotation setpoint. Digital transformation is broader organizational change: it can alter roles, workflows, maintenance practices, decision rights and investment priorities across a company.
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These terms are often mixed in mining discussions. A plant can digitize records without connecting them to control decisions, and it can install a model without changing how operators work. The practical subject here is digitalization: reliable data flowing through a plant’s operating and information systems to support better decisions and, in some cases, advanced control.
Why mineral processing needs connected information
Ore is not uniform. Grade, mineralogy, hardness, moisture and liberation can vary between benches, stockpiles and even truckloads. Equipment condition, water chemistry, circulating loads and wear also change. Operators must therefore make decisions with incomplete and delayed information.
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A connected digital workflow can combine fast process measurements with slower but more definitive laboratory results. It can expose trends that are hard to see in isolated screens, estimate variables that are not measured continuously, and let a team examine a proposed change before applying it to the physical circuit. These capabilities are intended to support goals such as stable operation, recovery, throughput and resource efficiency. They are goals, not guaranteed outcomes: results depend on the ore, instrumentation, process design, model quality and how the system is commissioned and used.
How the digitalization workflow works
1. Measure the ore, equipment and process
The workflow starts with observations. Instruments and information sources can include feed samplers, belt scales, particle-size measurements, density meters, pressure and flow transmitters, motor data, online elemental analyzers, laboratory assays, maintenance records and equipment tags from the automation system.
Measurements may span the complete value chain: ore characterization, comminution, separation, concentration, product creation and tailings. Laboratory data remains important because an online signal may be indirect, biased or unavailable for a particular constituent. Time stamps, calibration records, units and sample locations determine whether readings can be compared meaningfully.
2. Connect and organize the information
Automation systems operate equipment and monitor instruments. An information layer can link those tags with laboratory results, historian data, engineering information and project records so that users are working from consistent context rather than manually copying values between systems.
Data organization includes naming conventions, time synchronization, quality flags, access permissions and rules for handling missing or implausible values. A dashboard cannot repair a disconnected control system, an uncalibrated analyzer or a laboratory result attached to the wrong sampling time.
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3. Analyze data and represent the process with models
Analytics can identify patterns, estimate unmeasured variables and flag abnormal behavior. A process model represents relationships among feed properties, equipment settings and outputs. A soft sensor is a model-based estimate—for example, an inferred grade or particle-size value derived from several measurements when no continuous physical analyzer is available.
A digital twin is a digital representation of a real-world entity or system, a definition attributed to Gartner and reproduced by the Geological Survey of Finland (GTK). In a processing plant, the twin is generally a combination of process models, live or historical data, simulation and links to operating systems. It need not be a photorealistic three-dimensional model.
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Operators can view current conditions, compare model predictions and test “what-if” scenarios. A scenario might ask how a change in mill speed, water addition or reagent dosage could affect a downstream constraint. The result is decision support; the operator still approves the change unless a control strategy has been specifically authorized.
Metso describes advanced process control (APC) as a system that “automatically adjusts process variables to keep operations at optimal conditions.” In practice, APC normally operates within configured limits, uses selected measurements and setpoints, and hands control back or raises an alarm when data quality or operating conditions fall outside its design. The scope of automation is site-specific.
5. Compare results and update the models
Operating data can be compared with model outputs to find bias and changing relationships. Recalibration may be needed when ore type, liner condition, reagent regime or equipment performance changes. Adaptation algorithms described in Metso’s Newmont Lihir case are an example of a deployed vendor capability, not a feature that every digital twin automatically provides.
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What digitalization can cover in a processing plant
| Area | Typical digital functions | Decision supported |
|---|---|---|
| Crushing and screening | Monitor feed rate, power, particle size and equipment status; identify bottlenecks | Adjust feed or operating limits while protecting capacity and equipment |
| Grinding and classification | Combine power, pressure, density, flow and size measurements with models | Balance throughput, liberation and circulating load |
| Flotation | Use assay, reagent, air, level and froth information; estimate process response | Choose operating targets while accounting for changing feed |
| Thickening and filtration | Track density, torque, bed behavior, moisture and filter performance | Maintain product or tailings specifications and avoid instability |
| Refining and product handling | Link quality measurements, inventories and process constraints | Coordinate production with quality and downstream capacity |
The exact coverage depends on available instrumentation, control architecture, model scope and the plant’s operating objectives. A twin that models one grinding circuit is not equivalent to a plant-wide system.
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GTK Mintec, Finland (2021)
GTK’s 2021 description of its Mintec pilot-plant concept links laboratory work, plant automation, a digital twin, remote access and scenario testing. The facility is stated to handle up to 5 tonnes per hour. That figure describes GTK’s pilot plant, not a benchmark for commercial mineral-processing plants or a prediction of digitalization performance.
Newmont Lihir, Papua New Guinea
Metso says the Lihir operation implemented its Geminex metallurgical twin in 2023. The supplier’s case study was published on October 24, 2024 and updated in November 2025. The update says technical deployment was completed while full operational commissioning was still in progress; data integration and auto-optimization steps remained. It also describes a secure data lake, Azure and single sign-on. Any reported benefits or capabilities should therefore be read as claims about this named deployment and its commissioning status, not as independent proof that every twin delivers the same result.
Dundee Precious Metals operations, Bulgaria
Metso describes Grinding, Flotation, Thickener and Filter optimizers at the Ada Tepe and Chelopech operations, alongside Geminex and a Courier analyzer as of 2024. The account describes adaptation to changing inputs and operating setpoints. It is a supplier-authored case study, so it establishes that the described deployment and functions exist; it is not an independent evaluation of causal improvements.
What a digital twin does—and does not do
- It can: combine live and historical information, represent process relationships, estimate variables, monitor deviations and test scenarios before a change is made.
- It may: provide recommendations, feed an APC layer or adapt model parameters when those functions have been engineered and commissioned.
- It does not automatically: make poor measurements reliable, understand every ore type without calibration, replace operator accountability or run a plant autonomously.
- It is not necessarily: a 3D visualization. A useful twin may be mainly time-series data, equations, simulations and control-system interfaces.
How to start a digitalization project
- Define one operational decision. Choose a measurable problem such as unstable mill throughput, delayed quality information or a recurring thickener constraint. State the time horizon, limits and responsible operating team.
- Map the process and data path. List the unit operations, instruments, laboratory samples, control loops, historians and owners involved. Identify where tags are missing, delayed, duplicated or inaccessible.
- Establish data quality controls. Standardize units and timestamps, document calibration, flag sensor outages and define how bad data is excluded or replaced. Include laboratory quality procedures.
- Build and validate a baseline model. Use representative operating periods and test predictions against independent observations. Record the ore types, equipment condition and operating envelope used for calibration.
- Deploy monitoring before automatic action. Give operators trend views, alarms, explanations and scenario tools. Gather feedback on whether the estimates are understandable and useful.
- Test recommendations under controlled limits. Compare predicted and observed responses, define interlocks and specify when control must revert to the existing strategy.
- Commission APC or closed-loop functions in stages. Begin with a narrow set of variables and documented operating limits. Train operators, maintenance staff and engineers before expanding scope.
- Maintain and govern the system. Schedule model review, sensor calibration, cybersecurity assessments, access reviews and retraining when ore or equipment conditions change.
How to evaluate competing systems or implementation approaches
| Evaluation question | Why it matters |
|---|---|
| Which unit operations are covered? | A grinding optimizer, a flotation model and a plant-wide twin solve different problems. |
| Will it work with existing control, laboratory and information systems? | Integration determines whether data arrives with the timing and context required for decisions. |
| What measurements and data-quality functions are included? | Model sophistication cannot compensate for missing, biased or poorly synchronized inputs. |
| Can models be recalibrated for changing ore and equipment? | Static relationships can lose accuracy as feed, wear and operating practice change. |
| Does it monitor, simulate, recommend or control? | These are different risk levels and require different approvals, interlocks and training. |
| How are cybersecurity and data governance handled? | Connections to operational technology must fit mine-wide security policies and access rules. |
| What commissioning and ongoing support are provided? | Adoption, troubleshooting and model maintenance continue after installation. |
Common failure modes and practical fixes
Poor or disconnected measurements
Symptoms include implausible trends, frequent model resets and recommendations operators do not trust. Check sensor calibration, sample timing, historian gaps, tag mapping and laboratory data joins before changing the model.
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A model trained for only one ore regime
Accuracy can fall when mineralogy, hardness, moisture or equipment condition changes. Segment the operating envelope, retain representative calibration data and establish a review trigger for new ore types or major maintenance.
Automation introduced before operating ownership
If operators cannot explain a recommendation or override behavior, the system may be bypassed. Start with transparent monitoring and scenarios, document limits and involve shift teams in commissioning and training.
Integration and security treated as afterthoughts
Uncontrolled connections, unclear permissions or inconsistent identity management create operational and cybersecurity risk. Use the site’s approved architecture, least-privilege access, change control and tested fallback procedures.
Benefits presented as universal statistics
The consulted sources do not establish an independent, named statistical result that can be generalized for recovery, throughput or cost. Vendor and institutional accounts describe aims, capabilities or reported outcomes for particular sites. A credible business case should measure its own baseline, operating conditions and post-commissioning results.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteDoes digitalization mean an autonomous plant?
No. Digitalization can stop at connected monitoring, progress to model-based recommendations, or include APC that adjusts selected variables inside approved constraints. Human operators, engineers and maintenance teams remain responsible for interpreting alarms, managing abnormal situations and deciding how the system is expanded. Autonomy is a site-specific control-system outcome, not a synonym for having a digital twin.
Bottom line for plant decision-makers
Digitalization works when trustworthy measurements, integrated information, process understanding and disciplined control engineering are treated as one system. Start with a defined operating decision, prove data quality and model behavior on representative ore, then add recommendations and automatic control in stages. The technology can improve visibility and consistency, but its value depends on calibration, cybersecurity, commissioning and sustained workforce adoption—not on the label “digital twin” alone.
Frequently Asked Questions
What data is required before building a mineral-processing digital twin?
At minimum, the project needs time-aligned process measurements, equipment and control-system tags, representative laboratory or assay results, documented units and quality flags, and enough operating history to cover the ore and equipment conditions the model must represent.
How often should a processing model be recalibrated?
There is no universal interval. Review accuracy when ore mineralogy or hardness changes, equipment is modified or worn, reagent and water regimes shift, sensors are replaced, or predictions show sustained bias. The review schedule should be defined during commissioning.
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No. Recovery, throughput, stability and resource-efficiency improvements are goals reported by institutions or vendors for particular deployments. Outcomes vary with instrumentation, integration, model quality, operating practice and commissioning, so each site needs its own baseline and measurement plan.
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