AI · IoT · Digital Twin · Automation · Predictive Analytics

Transforming Mining with AI & Digital Intelligence

KDMine Group delivers end-to-end digital transformation for mining operations — from AI-powered ore grade prediction and autonomous fleet management to digital twin simulation and real-time production dashboards. We bridge the gap between traditional mining engineering and Industry 4.0.

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Live Technology Stack

Machine Learning — Grade PredictionActive

IoT Sensor Network — Fleet TrackingActive

Digital Twin — Process PlantBeta

NLP — Drill Report ParsingActive

Computer Vision — Safety MonitoringActive

Predictive Maintenance — SAG MillActive

Digital Solutions Portfolio

AI & Digital Mining Solutions

Six integrated digital solution areas — each designed to address specific operational and economic challenges in modern mining.

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AI Ore Grade Prediction

Machine learning models trained on drill core, geophysics, and historical assay data to predict ore grade ahead of the mining face — reducing dilution and improving mill feed quality.

Random ForestXGBoostNeural NetKriging+ML

Grade prediction accuracy: 88–94%

Dilution reduction: 15–25%

Real-time blast hole assay integration

3D grade uncertainty mapping

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Autonomous Fleet Management

AI-powered haul truck dispatch optimisation using real-time GPS, payload, and cycle time data. Integrates with Caterpillar MineStar, Komatsu FrontRunner, and Wenco FMS platforms.

MineStarFrontRunnerWencoDISPATCH

Truck productivity improvement: 12–22%

Fuel consumption reduction: 8–15%

Real-time queue time minimisation

Autonomous haulage readiness assessment

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Process Plant Digital Twin

High-fidelity dynamic simulation model of the processing plant — calibrated to plant data and used for operator training, process optimisation, and what-if scenario analysis.

USIM PACJKSimMetAspen PlusUnity 3D

Recovery improvement: 1.5–3.5%

Operator training time reduction: 40%

Throughput bottleneck identification

Real-time process optimisation

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Predictive Maintenance (PdM)

IoT sensor-based condition monitoring and ML failure prediction for critical mining equipment — SAG mills, haul trucks, conveyor systems, and large pumps. Reduces unplanned downtime by 30–50%.

VibrationThermalLSTMSCADA

Unplanned downtime reduction: 30–50%

Maintenance cost reduction: 20–35%

SAG mill bearing failure prediction (14-day horizon)

Haul truck tyre life prediction

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IoT & Real-Time Production Dashboard

End-to-end IoT architecture connecting mine sensors, plant instruments, and fleet telematics to a unified real-time production dashboard — accessible on any device.

MQTTOPC-UAAzure IoTGrafana

Sub-second data latency

1,000+ sensor integration capacity

KPI alerting and anomaly detection

Mobile-first responsive design

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Computer Vision Safety & Compliance

AI-powered camera systems for real-time PPE detection, exclusion zone monitoring, fatigue detection, and blast area clearance verification — integrated with site access control systems.

YOLOv8OpenCVEdge AIRTSP

PPE compliance detection: 97% accuracy

Fatigue detection: 94% accuracy

Works on existing CCTV infrastructure

ISO 45001 compliance reporting

Implementation Roadmap

KDMine AI Implementation Pipeline

Our structured 5-phase AI implementation methodology ensures rapid deployment, measurable ROI, and sustainable digital capability within your organisation.

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Digital Maturity Assessment

Audit existing data infrastructure, OT/IT systems, and identify highest-value AI use cases with ROI modelling.

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Data Architecture & Integration

Design and deploy data lake, historian integration, and real-time streaming pipelines from mine sensors to cloud.

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AI Model Development

Build, train, and validate ML models using site-specific data. Explainability and uncertainty quantification included.

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Production Deployment

Deploy models to production with CI/CD pipelines, real-time inference, and integration with existing SCADA/MES systems.

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Monitor, Retrain & Scale

Continuous model performance monitoring, automated retraining triggers, and scaling to additional use cases and sites.

Proven Results

AI Use Cases with Measured Outcomes

Real-world deployments at operating mines — with independently verified performance improvements.

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Sar Cheshmeh — ML Grade Control

Deployed XGBoost grade prediction model using 8 years of blast hole assay data. Integrated with Vulcan block model for real-time ore/waste classification at the shovel.

✓ 22% dilution reduction · $18M/yr value

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Gol-E-Gohar — AI Truck Dispatch

Replaced manual dispatch with AI optimisation engine processing 2,400 dispatch decisions per hour across 42-truck fleet. Integrated with Wenco FMS.

✓ 17% productivity gain · 11% fuel saving

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Miduk Copper — SAG Mill PdM

LSTM neural network trained on 3 years of SAG mill vibration, temperature, and power draw data. Predicts bearing failures 14 days in advance.

✓ 43% unplanned downtime reduction

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Chadormalu — IoT Production Hub

Deployed 1,200-sensor IoT network across mine, crusher, and pellet plant. Unified real-time dashboard replacing 6 separate legacy reporting systems.

✓ 28% reporting time reduction · 99.8% uptime

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Balkhash Copper — Digital Twin

Full flotation circuit digital twin built in USIM PAC and calibrated to 18 months of plant data. Used for operator training and reagent optimisation.

✓ 2.1% recovery improvement · $9M/yr

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Multi-Site — CV Safety System

Deployed YOLOv8-based PPE and exclusion zone detection across 8 mine sites using existing CCTV infrastructure. Zero additional hardware required at 6 of 8 sites.

✓ 97% PPE compliance · 0 exclusion incidents

Technology Partnerships

Platforms & Technology Stack

KDMine integrates with all major mining technology platforms — we are platform-agnostic and vendor-independent.

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Cloud & Data

Azure IoT Hub, AWS SageMaker, Google Vertex AI, Databricks, Snowflake

Microsoft Partner

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Mine Planning

Vulcan, Surpac, Datamine, Leapfrog, MineSight, Deswik, Whittle

Certified

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Fleet Management

Caterpillar MineStar, Komatsu FrontRunner, Wenco FMS, Modular Mining DISPATCH

Integrated

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Process Simulation

USIM PAC, JKSimMet, Aspen Plus, METSIM, HSC Chemistry, Limn

Expert Users

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Dashboards & BI

Power BI, Grafana, Tableau, OSIsoft PI, Ignition SCADA, Aveva

Deployed

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AI / ML Frameworks

TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM, Hugging Face

In-House

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IoT Protocols

MQTT, OPC-UA, Modbus, PROFINET, DNP3, IEC 61850

Certified

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Cybersecurity

IEC 62443, NIST CSF, OT/IT segmentation, zero-trust architecture

ISO 27001

Common Questions

Digital Mining & AI FAQ

How much data do we need before AI models become useful?

For grade prediction models, a minimum of 2 years of blast hole assay data with corresponding production records is recommended. For predictive maintenance, 12–18 months of sensor data capturing at least 3–5 failure events is the minimum. For fleet dispatch optimisation, 6 months of GPS and payload data is sufficient. KDMine can also apply transfer learning from analogous operations to accelerate model development when site data is limited.

Can AI be implemented without replacing existing systems?

Yes — this is our preferred approach. KDMine's AI solutions are designed as an intelligence layer on top of existing OT/IT infrastructure. We integrate with existing SCADA, FMS, historian, and ERP systems via standard protocols (OPC-UA, REST API, MQTT) without requiring system replacement. This reduces implementation risk, cost, and disruption to operations. Our computer vision safety system, for example, works with existing CCTV cameras at 75% of sites with no hardware changes.

What is the typical ROI timeline for a digital mining project?

ROI timelines vary by solution: Fleet dispatch optimisation typically achieves payback in 6–12 months. Predictive maintenance projects typically pay back in 12–18 months. Grade prediction and digital twin projects typically pay back in 18–24 months. Computer vision safety systems typically pay back in 24–36 months (primarily through insurance premium reduction and incident cost avoidance). KDMine provides a detailed ROI model as part of the digital maturity assessment.

Do you work with mines that have limited internet connectivity?

Yes. KDMine designs edge-first architectures for remote mine sites with limited or unreliable internet connectivity. AI inference runs locally on edge hardware (NVIDIA Jetson, industrial PCs) with periodic synchronisation to cloud when connectivity is available. Our IoT systems use store-and-forward protocols to ensure no data loss during connectivity outages. We have deployed solutions at sites with only 2-hour daily satellite connectivity windows.