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Digitalization in mineral processing connects measurements from ore, equipment and laboratories with data platforms, process models, analytics and control systems. The purpose is to help people see plant conditions, test likely responses, predict changes and, where the design allows, adjust selected variables automatically. It can cover the full route from crushing and grinding through flotation, dewatering, product handling and refining; it is a plant-wide operating method, not simply the purchase of a digital twin.
Digitalization, digitization and digital transformation are different
These terms are often used interchangeably, but they describe different levels of change:
- Digitization converts something that was not digital into digital form—for example, scanning a paper laboratory sheet or replacing a manual gauge reading with an electronic sensor.
- Digitalization uses connected digital information to improve work. A flotation analyzer, control-system tag, laboratory result and operator dashboard might share data so a setpoint can be changed with better context.
- Digital transformation is broader organizational change: new operating practices, responsibilities, skills, governance and investment decisions built around digital ways of working.
The practical mineral-processing question is therefore not “Do we have a digital twin?” It is “Can trustworthy information move from the ore and equipment to a useful decision, and can the resulting action be applied safely?”
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Digitalization follows the material and decision flow through the operation. GTK describes collecting information from ore characterization through comminution, separation, concentration, product creation and tailings. Typical information includes:
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| Plant area | Examples of measurements or records | Decisions they can support |
|---|---|---|
| Ore characterization | Mineralogy, hardness, liberation, moisture, grade and feed-size data | Blend selection, mill strategy and expected recovery |
| Crushing and screening | Power, pressure, throughput, product size and equipment status | Feed-rate changes, wear planning and blockage response |
| Grinding | Mill power, load, density, particle size, cyclone pressure and water addition | Speed, water, media or feed adjustments while protecting capacity |
| Flotation and separation | Flow, chemistry, air rate, froth images, density and analyzer results | Reagent, air and level changes aimed at grade and recovery targets |
| Thickening, filtration and dewatering | Underflow density, torque, pressure, moisture and filtrate quality | Polymer dosing, bed level, filter cycle and product-moisture control |
| Refining and product handling | Composition, temperature, energy use, inventory and quality samples | Recipe, energy and quality adjustments, plus production accounting |
| Tailings and utilities | Water balance, pump status, storage level, power and emissions data | Water reuse, pumping reliability and environmental reporting |
Control-system tags are only one part of the picture. Laboratory assays, maintenance records, geological models, operator comments, shift reports and historical production data can be just as important. A platform that cannot align those sources by time, equipment and material stream will produce attractive charts but weak decisions.
How the digitalization workflow works
1. Measure the ore and the operation
Sensors, online analyzers, laboratory instruments and equipment tags generate the raw observations. Measurements may be continuous, sampled periodically or entered manually. The first engineering task is to define what must be known, how often it must be measured, the expected accuracy and what happens when a signal is missing.
Measurement coverage is especially difficult when ore properties change faster than laboratory results arrive. A model cannot infer a new ore type reliably if the plant has no representative assays, mineralogical data or operating history for calibration.
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2. Connect and organize the data
Automation and information-management systems connect field instruments with control equipment, laboratory systems and historical or project data. In a conventional architecture, a distributed control system or programmable logic controllers run equipment, a historian stores time-series tags, and separate laboratory or maintenance systems hold other records. Digitalization adds governed interfaces and a common context: which tag belongs to which asset, which assay belongs to which sample and which time zone or campaign applies.
Data preparation is unglamorous but decisive. Units, timestamps, sensor status, calibration records, bad-value flags and changes in equipment configuration must travel with the measurement. A dashboard that silently treats a failed analyzer as zero can be more dangerous than no dashboard.
3. Analyze conditions and model the process
Analytics can describe what is happening, detect patterns and estimate variables that are difficult or expensive to measure directly. Process models represent relationships among feed properties, equipment settings and outputs. A soft sensor, for example, may estimate a quality variable between laboratory samples using available process signals.
A digital twin is a digital representation of a real-world entity or system; GTK reproduces this definition with attribution to Gartner. In a processing plant, the twin may be a collection of first-principles equations, empirical models, real-time data connections, simulation tools and interfaces. It does not have to be a photorealistic three-dimensional model. Its useful test is whether it represents the process well enough to monitor conditions or examine a proposed change.
4. Decide, recommend or control
Operators can use dashboards, alarms, predicted trends and “what-if” scenarios to select a setpoint. A system may stop at a recommendation, require operator approval, or write an approved value directly to the control system. Those are materially different levels of automation and should be specified in the project design.
Metso defines advanced process control (APC) as automatically adjusting process variables to keep operations at optimal conditions. In practice, APC normally works within configured limits and objectives. Interlocks, permissives and the underlying regulatory control loops remain essential; APC is not a replacement for safe plant control.
5. Compare results and update the model
Actual production, assays and equipment responses are compared with predictions. Engineers can then recalibrate parameters, investigate bias or retire a model that no longer represents the plant. This feedback loop matters because ore hardness, mineralogy, water chemistry, equipment condition and operating strategy all change.
The Newmont Lihir case describes adaptation algorithms responding to process changes. That is a capability reported for that deployed system, not a property that every digital twin automatically possesses.
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| Capability | What it means in operation | What it does not imply |
|---|---|---|
| Monitoring | Combines live data and model calculations to show current or estimated conditions. | That every sensor is accurate or every variable is observable. |
| Scenario analysis | Tests a feed, setpoint or equipment scenario before an engineer or operator applies it. | That the simulated result will occur exactly in the physical plant. |
| Prediction | Projects trends such as throughput, grade, recovery, energy or a constraint breach. | A guaranteed improvement or a substitute for sampling and engineering judgment. |
| Recommendation | Suggests an operating change for a person to review and approve. | Permission to bypass procedures, limits or accountability. |
| Closed-loop control | Writes selected values to an APC or control layer within designed constraints. | Fully autonomous plant operation. |
Whether a plant reaches any row in this table depends on instrumentation, model validation, control-system integration, cybersecurity, commissioning and the site’s operating philosophy.
Why ore variability makes calibration central
A model calibrated on one ore campaign can become unreliable when hardness, mineralogy, clay content, moisture or liberation changes. The consequences can include a biased recovery estimate, an incorrect reagent recommendation or an apparently unexplained constraint in the mill.
Effective variability management combines several practices:
- Record feed characteristics and blend information alongside plant tags.
- Use laboratory and mineralogical results to check online analyzers and soft sensors.
- Separate model behavior by ore domain, campaign or operating regime when one equation is inadequate.
- Monitor prediction error and define thresholds that return control to an operator or a simpler fallback strategy.
- Recommission after major equipment changes, liner changes, circuit modifications or sustained changes in water and reagent chemistry.
Adaptation should be governed, not invisible. A model that changes itself without traceable versions, approval rules and rollback procedures is difficult to audit and risky to use for automatic control.
Reported deployments and what their evidence shows
GTK Mintec pilot plant, Finland
A 2021 account from the Geological Survey of Finland (GTK) describes a pilot-plant concept linking laboratory work, plant automation, a digital twin, remote access and scenario testing. The facility states a feeding capacity of up to 5 tonnes per hour. That number describes the GTK pilot facility; it is not a benchmark for commercial plants or a promised capacity gain from digitalization.
The example is useful because it treats digitalization as a chain from ore characterization to plant testing and stakeholder access, rather than as a standalone visualization project.
Newmont Lihir, Papua New Guinea
Metso says the Lihir operation implemented its Geminex metallurgical twin in 2023. The case study was published on October 24, 2024, and updated in November 2025. The update reports that technical deployment was complete while full operational commissioning was still in progress; data integration and auto-optimization steps remained.
This timeline illustrates an important distinction: installing software and connecting data is not the same as proving stable operational use. Claims about benefits in this example should be read as claims from Metso’s supplier-authored case study, not as independent statistical proof that every operation will achieve the same result.
Dundee Precious Metals operations, Bulgaria
Metso describes Grinding, Flotation, Thickener and Filter optimizers at the Ada Tepe and Chelopech operations, used alongside Geminex and a Courier analyzer as of 2024. The account describes adaptation to changing inputs and operating setpoints. It is evidence that the named deployment and capabilities exist; it is not an independent evaluation of causal performance improvement.
How to start digitalization in a processing plant
1. Define a decision, not a technology purchase
Choose a constrained problem with a measurable decision: stabilize mill product size, reduce analyzer-to-laboratory bias, manage flotation air and level, or improve thickener control. Define the operating limits, responsible role and success measures before selecting a platform.
2. Map the current control and information systems
Document instruments, PLCs, distributed control, historians, laboratory information, maintenance records, network zones and manual workarounds. Identify ownership of each data source and the interfaces available for read-only and write-back operations.
3. Audit data quality and observability
Check completeness, time alignment, outliers, calibration status, sensor drift, laboratory turnaround and periods when equipment was bypassed. Decide which missing values are interpolated, held, rejected or replaced by a fallback model. Do not begin closed-loop control while key constraints are poorly measured.
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4. Build a governed pilot
Start in monitoring or recommendation mode. Compare model predictions with laboratory and production results across more than one ore condition. Keep a versioned record of model parameters, data changes, operator overrides and incidents. A pilot should have a stop condition and a rollback path, not only a launch date.
5. Commission automation in stages
- Validate sensor signals and alarms in the existing control system.
- Run the model in shadow mode, where it predicts but cannot change setpoints.
- Use operator-approved recommendations within conservative limits.
- Enable automatic moves for a small set of variables after testing interlocks, permissives and failure behavior.
- Review performance by ore type, shift and equipment condition before expanding scope.
6. Train people and assign ownership
Operators need to understand what a recommendation means, when to reject it and how to return to manual control. Process engineers need model-diagnosis skills; instrumentation and IT teams need responsibility for data and interfaces. Adoption fails when a tool creates alerts without a clear response owner.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Selection criteria for systems and implementation approaches
When comparing an APC package, a digital twin, an analytics platform or an integrated project, use the same operational questions:
| Criterion | Questions to ask |
|---|---|
| Process scope | Which unit operations, streams and downstream processes are covered, and can scope expand without rebuilding the data model? |
| Compatibility | Can it exchange data with existing control, historian, laboratory, maintenance and identity systems? |
| Measurement and data quality | How are bad values, missing signals, units, timestamps, calibration and laboratory reconciliation handled? |
| Calibration | Can models account for changing ore, equipment condition, blends and operating regimes? |
| Action level | Does it monitor, simulate, recommend or execute closed-loop control, and are the boundaries configurable? |
| Security and governance | What network separation, authentication, access logging, encryption, patching and data-retention controls are provided? |
| Commissioning and support | Who validates the model, trains staff, handles drift and supports the system after handover? |
Common failure modes and practical fixes
- “More data” without a decision: prioritize a plant question and remove unused signals rather than building an unmanageable dashboard.
- Unreliable instrumentation: repair, calibrate or replace the measurement; analytics cannot make a disconnected or biased signal trustworthy.
- Model works only on historical data: test on later campaigns and different ore domains, then set explicit limits for extrapolation.
- Integration stops at a screen: connect recommendations to existing procedures and control layers, with approval and rollback paths.
- Cybersecurity added late: align architecture with mine IT policies from the start. The Lihir account describes a secure data lake, Azure and single sign-on; each site must still define its own controls.
- Commissioning is declared too early: distinguish technical installation from stable operational use, user adoption and auto-optimization.
- Benefits are generalized from a case study: attribute supplier-reported outcomes, publish the measurement period and conditions, and avoid promising universal gains.
What benefits should a site measure?
Potential objectives include steadier throughput, recovery, product quality, energy and water efficiency, reduced variability, earlier fault detection and safer decision-making. They are objectives, not automatic results. Establish a baseline and track the same definitions after deployment, including ore type, equipment availability, production rate and laboratory method.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThere is no independent, named statistical result in the cited material that supports a general percentage improvement in recovery, throughput or cost for digitalization across mines. A credible business case therefore combines site-specific baseline data with a staged risk assessment rather than copying a vendor headline.
Bottom line
Digitalization works when trustworthy measurements are connected to process understanding and a controlled decision loop. Sensors and laboratory data provide the evidence; integrated systems organize it; models and analytics turn it into estimates or scenarios; operators and APC decide what action is safe; and ongoing comparison keeps the system calibrated as ore and equipment change. A digital twin can be a powerful part of that chain, but it is neither a guarantee of higher recovery nor proof that a plant operates autonomously.
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