Mining Ore Processing: Predictive Scrap AI for Zero Defects
By Grace on June 6, 2026
The monthly scrap report lands on the plant executive's desk with the same number it has carried for six consecutive quarters. 4.2% of throughput sent to the waste pile. The number has become a baseline, an accepted cost of doing business in mineral processing. But the story behind that stable number is not stability. It is a pattern that repeats every month with the same predictability. The flotation circuit drifts on Tuesday afternoons when the ore blend shifts. The grinding mill produces oversize particles on Thursday nights when the liner wear crosses a threshold that no one has calculated. The thickener underflow density drops on Sunday mornings when the weekend crew runs a different reagent ratio. Every one of these events generates scrap, and every one of them is preceded by a signature in the process data that appears three to six hours before the off-spec material reaches the concentrate line. The plant has the data to predict every one of those events. The gap is not in instrumentation. It is in the system that connects the data to the prediction. Predictive scrap analytics replaces reactive scrap management with machine learning models that continuously analyse historical and real-time process data to forecast scrap risk hours before it occurs, enabling plant executives to cut defect rates by 30 to 70% without changing a single piece of equipment or adding a single reagent.
Prediction Dashboard
Current Scrap Rate
4.2%
Baseline before predictive deployment
Target Scrap Rate
1.3-2.9%
30-70% reduction achievable with predictive scrap analytics
Prediction Window
3-6 hrs
Advance warning before off-spec material is produced
Model Accuracy
92%+
Classification accuracy for scrap risk prediction in mineral processing
The Scrap Report That Shows 4.2% Every Month Is Not a Stable Metric. It Is a Record of Predictable Failures That the Plant Has the Data to Prevent. Predictive Scrap Analytics Closes the Gap Between What the Data Says and What the Plant Does.
iFactory manages every sensor, model, and alert in your predictive scrap analytics pipeline with automated model training, scrap attribution tracking, and compliance audit trails for ISO 9001, CORSIA, and CSRD frameworks.
What Is Predictive Scrap Analytics for Ore Processing?
Predictive scrap analytics applies machine learning models to historical and real-time process data to forecast when and where off-spec material will be produced, giving plant teams a window of hours to intervene before scrap occurs. The models are trained on years of process data, scrap records, ore body characteristics, equipment condition logs, and reagent batch histories to identify the signatures that precede scrap events. Once trained, the models continuously score incoming data against those signatures, generating a scrap risk score for each process area every few minutes. When the risk score exceeds a configurable threshold, the system generates an alert with the predicted scrap type, the contributing process variables, and the estimated time to event. For plant executives, predictive scrap analytics transforms the scrap report from a backward-looking record of losses into a forward-looking tool that tells the team what to prevent, where to intervene, and how much time remains to act.
1
Historical Data
Years of process data, scrap records, ore body logs, and equipment history fed into model training
2
Real-Time Scoring
Live sensor data continuously scored against trained signatures every 2 to 5 minutes per process area
3
Risk Alert
Configurable threshold triggers alert with scrap type, contributing variables, and time-to-event estimate
4
Preventive Action
Operator or automated intervention based on predicted scrap type and remaining intervention window
The Scrap Prediction Timeline
Predictive scrap analytics does not simply flag that a defect will occur. It estimates when it will occur, allowing plant teams to prioritise interventions based on the remaining time window. The timeline below shows how a typical prediction evolves from initial risk detection to the point where off-spec material reaches the concentrate line.
T-6 Hrs
Initial Risk Detection
The ore body transition is detected by the predictive model through subtle shifts in mill power draw, cyclone density, and froth velocity. The scrap risk score rises from baseline 15 to 45. The system generates a low-priority alert: monitor flotation circuit parameters.
T-4 Hrs
Risk Escalation
The model correlates the ore change with the current reagent recipe and identifies a mismatch probability of 78%. Scrap risk score reaches 72. The system escalates to a medium-priority alert with the predicted scrap type: grade deviation in the flotation concentrate.
T-2 Hrs
Action Window
The model identifies the specific reagent dosage adjustment required to compensate for the ore change. Scrap risk score reaches 88. High-priority alert generated with recommended corrective action. Two hours remain before off-spec material reaches the concentrate line.
T-0
Scrap Event
Without intervention, the off-spec material reaches the concentrate line. Predicted 85 tonnes of grade-deviation scrap at $45 per tonne margin loss. With intervention at T-2 hours, the event is prevented entirely. The model records the near-miss for future training.
Scrap Risk by Process Area
Scrap risk is not evenly distributed across the concentrator. Understanding which process areas carry the highest scrap probability and the greatest financial impact enables plant executives to prioritise predictive model deployment where it delivers the fastest return.
High Risk
Probability: 35-45%
Flotation Circuit
Grade deviation due to ore body transitions and reagent mismatch is the single largest source of scrap in most concentrators. Predictive models achieve 94% accuracy on flotation grade deviation with a 4 to 6 hour prediction window.
Medium-High Risk
Probability: 25-35%
Grinding Circuit
Oversize particle generation from mill liner wear and feed hardness variation. Predictive models detect the signature of liner degradation 3 to 5 hours before grind size exceeds specification.
Medium-High Risk
Probability: 20-30%
Thickener Circuit
Underflow density deviations and overflow clarity excursions caused by flocculant dosage errors and feed solids variation. Predictive window of 2 to 4 hours before off-spec conditions.
Medium Risk
Probability: 15-25%
Crushing Circuit
Oversize material from crusher gap setting drift and liner wear. Predictive models correlate power draw patterns with product size distribution to forecast oversize events 2 to 3 hours in advance.
Lower Risk
Probability: 10-15%
Conveying System
Spillage and material degradation from belt wear, misalignment, and transfer point issues. Predictive models detect spillage precursors 1 to 2 hours before material loss becomes significant.
Lower Risk
Probability: 5-10%
Tailings System
Containment and quality deviations from thickener underflow excursions. Predictive window of 1 to 3 hours before environmental or quality parameters exceed limits.
Scrap Root Cause Decomposition
Predictive scrap analytics not only forecasts when scrap will occur but also identifies the root cause category. This decomposition enables plant executives to target interventions at the highest-impact causes rather than treating every scrap event as if it had the same origin.
Root Cause
Each scrap event in a mineral processing plant has a primary root cause. Predictive models attribute every alert to one of five categories based on the combination of process variables that triggered the risk score.
Feed Variability
35%
Ore hardness, mineralogy, and particle size distribution changes from the mine
Flocculant dosage, collector chemistry, frother concentration, and pH control
Operating Parameters
15%
Setpoint drift, control limit violations, and operator-to-operator variation
Other
5%
Water quality, seasonal effects, instrumentation drift, and external factors
Prediction Accuracy Metrics
The effectiveness of predictive scrap analytics is measured through four key metrics that plant executives can track from deployment day one. Each metric connects directly to the financial performance of the concentrator.
A
Precision Rate
Of every 100 scrap alerts, how many correctly predict an actual scrap event
89-94%
Precision range across trained process areas
B
Recall Rate
Of every 100 actual scrap events, how many are predicted before they occur
85-92%
Recall range across different ore types and operating conditions
C
Mean Time to Event
Average advance warning time between alert generation and predicted scrap
3.8 hrs
Average prediction window across all trained process areas
D
Scrap Attribution Rate
Percentage of total scrap that the model can attribute to a specific root cause
Plant executives deploying predictive scrap analytics follow a structured five-phase approach that builds the model library incrementally, expanding coverage as each model proves its accuracy in production.
1
Data Audit and Model Scoping
Audit available historical data: process variables, scrap records, ore body logs, equipment condition data. Identify which process areas have sufficient data quality for model training. Define scrap categories and prediction window requirements. Duration: 2 to 3 weeks.
2
Model Training and Validation
Train initial models on 12 to 24 months of historical data for the highest-scrap process areas. Validate against held-back test data. Achieve target precision and recall before proceeding to parallel run. Duration: 4 to 6 weeks per model.
3
Shadow-Mode Parallel Run
Run predictive models alongside existing scrap management processes for 4 to 6 weeks. Compare predicted events against actual scrap records. Fine-tune thresholds and retrain models based on false positive and false negative analysis.
4
Active Prediction and Alerting
Activate predictive alerts for operations team. Integrate scrap risk scores into control room dashboards. Establish escalation protocols for high-risk predictions. Track precision and recall weekly. Expand to additional process areas. Duration: 4 to 6 weeks per area.
5
Continuous Model Improvement
Retrain models monthly with accumulated prediction outcomes and new scrap records. Expand training dataset as new ore types and operating conditions are encountered. iFactory manages model versioning, retraining schedules, and scrap attribution reporting for ISO 9001 and CSRD compliance.
The 4.2% Scrap Rate Has Been Stable for Six Quarters Not Because the Process Is Under Control but Because No One Has Connected the Data to the Prediction. Predictive Scrap Analytics Turns That Stability Into a Starting Point, Not a Baseline.
iFactory manages every sensor, model, and alert in your predictive scrap analytics pipeline with automated model training, scrap attribution tracking, and compliance audit trails for ISO 9001, CORSIA, and CSRD frameworks.
Traditional SPC charts monitor current process variables against control limits and trigger alarms when a variable exceeds its boundary. The alarm tells you that a problem exists now. Predictive scrap analytics forecasts that a problem will exist in the future. It does this by training machine learning models on historical patterns that precede scrap events, enabling the system to recognise the early signatures of developing defects before any variable has crossed its control limit. The difference is the difference between a smoke detector that activates when the fire is burning and one that detects the gas leak before the fire starts. SPC tells you what is happening. Predictive scrap analytics tells you what is about to happen and gives you hours to prevent it. Most plants deploy both systems together: SPC for real-time monitoring and predictive analytics for forward-looking prevention. iFactory manages the integration between SPC systems and predictive models to provide a unified view of current and future scrap risk. Book a Demo to see how iFactory integrates predictive scrap analytics with existing SPC infrastructure.
The minimum recommended training dataset is 12 to 24 months of continuous process data at the highest available sampling frequency, accompanied by corresponding scrap records that identify each scrap event by type, quantity, process area, and time of occurrence. The process data should include all variables that are correlated with quality outcomes in each area: mill power, cyclone density, froth depth, reagent flow rates, pH, temperatures, pressures, and feed rates. Contextual data improves model accuracy significantly: ore type classifications from the mine plan, equipment hours since last maintenance, reagent batch identifiers, water source, and shift team composition. Plants with less than 12 months of data can deploy initial models using a simpler approach that starts with 3 to 6 months of data and progressively expands as more data accumulates. The models will still deliver meaningful predictions from week one and improve measurably as the training window extends. iFactory manages data quality assessment, feature engineering, and model training to ensure every model meets accuracy targets before deployment. Get In Touch to discuss how iFactory handles data requirements for predictive scrap model training.
Well-trained predictive scrap models achieve recall rates of 85 to 92%, meaning 85 to 92% of actual scrap events are predicted before they occur. The remaining 8 to 15% of events are typically caused by sudden, unpredictable conditions that do not have a detectable signature in the process data: operator errors that happen without warning, instrumentation failures that occur instantly, or external events like power interruptions. The precision rate, the percentage of alerts that correctly predict an actual scrap event, ranges from 89 to 94% across trained process areas. This means 89 to 94% of alerts are actionable and approximately 6 to 11% are false positives that require investigation but no corrective action. The scrap attribution rate, the percentage of total scrap that the model can attribute to a specific root cause, ranges from 78 to 85%. This attribution capability is critical for targeting corrective actions at the highest-impact causes. These accuracy ranges are based on published results from mineral processing operations that have deployed predictive scrap analytics across grinding, flotation, and thickening circuits. Get In Touch to request accuracy benchmarks for your specific process areas and ore types.
The deployment timeline depends on data availability and the number of process areas covered, but most plants follow a standard schedule. The data audit and model scoping phase takes 2 to 3 weeks. Model training and validation for the first process area takes 4 to 6 weeks. The shadow-mode parallel run takes 4 to 6 weeks, during which the models generate predictions that are compared against actual scrap records but are not yet used for operational decisions. Active prediction and alerting begins approximately 12 to 16 weeks from project initiation for the first process area. Additional process areas are added every 4 to 6 weeks thereafter. Plants typically see the first measurable scrap reduction within 8 to 12 weeks of active prediction, once the operations team has adjusted to the alerting workflow and interventions are being executed consistently. iFactory provides scrap reduction dashboards that track prediction accuracy, intervention effectiveness, and scrap rate by process area from week one of active prediction. Book a Demo to see how iFactory manages the predictive scrap analytics deployment timeline and milestone tracking.
Predictive scrap analytics models are designed for continuous retraining. As new ore types, equipment conditions, and operating parameters are encountered, the model training dataset is expanded to include these new conditions. The retraining process uses transfer learning from the existing production model, which reduces the amount of new data required to maintain accuracy. The standard retraining cadence is monthly, with an ad-hoc retraining triggered whenever the model's precision or recall drops below a configurable threshold. The model version history provides a complete audit trail for ISO 9001 compliance, documenting every training dataset, validation result, and accuracy metric. When a new ore body is introduced, the model may initially show reduced accuracy for 2 to 4 weeks until sufficient training data under the new conditions accumulates. After the retraining cycle, accuracy returns to target levels. iFactory manages model versioning, retraining schedules, and performance monitoring to ensure every model in production maintains the accuracy level defined in the quality plan. Book a Demo to see how iFactory manages the predictive model lifecycle and continuous retraining workflow.
The Data to Predict Every Scrap Event in Your Plant Already Exists. The Question Is Whether You Are Using It to Prevent Loss or Simply to Measure It.
iFactory manages every sensor, model, and alert in your predictive scrap analytics pipeline with automated model training, scrap attribution tracking, and compliance audit trails for ISO 9001, CORSIA, and CSRD frameworks.
