Introduction

The Mineral Liberation Analysis (MLA) Training Course is designed to equip mining, metallurgy, and mineral processing professionals with advanced skills in automated mineralogy, ore characterization, digital mineral analysis, process optimization, geometallurgy, AI-driven mineral processing, sustainable mining technologies, and data-driven beneficiation strategies. This intensive course integrates theoretical knowledge with practical laboratory applications using modern MLA systems, scanning electron microscopy (SEM), image analysis software, and machine learning approaches for mineral identification and liberation assessment. Participants will gain industry-relevant competencies to improve operational efficiency, recovery optimization, resource evaluation, and decision-making in modern mining environments.

The course focuses on emerging trends shaping the global mining industry, including Industry 4.0 mining, smart mineral processing, ESG compliance, critical minerals analysis, battery mineral characterization, predictive analytics, digital twins, circular economy mining, and sustainable beneficiation technologies. Through real-world case studies, hands-on software training, and laboratory demonstrations, learners will understand how MLA supports mineral exploration, flotation optimization, hydrometallurgy, tailings management, and process plant performance enhancement. The program is structured to build expertise aligned with international mining standards and the evolving needs of mining companies, research institutions, and mineral laboratories.

Programme Curriculum

Mineral Liberation Analysis (MLA) Training Course

Introduction

Course Duration

5 days

Course Objectives

Develop expertise in Mineral Liberation Analysis (MLA) methodologies and applications.
Understand advanced automated mineralogy and SEM-based mineral characterization techniques.
Apply AI and machine learning in mineral processing optimization.
Improve skills in geometallurgical data interpretation and ore variability analysis.
Master quantitative mineralogical analysis for beneficiation processes.
Optimize flotation, gravity separation, and hydrometallurgical operations using MLA data.
Learn modern approaches in critical minerals and battery minerals characterization.
Analyze tailings reprocessing and circular economy mining strategies.
Enhance operational efficiency using digital mining and Industry 4.0 technologies.
Strengthen competencies in process mineralogy and sustainable resource management.
Utilize advanced software tools for data analytics and mineral image processing.
Develop problem-solving skills using real-time plant performance monitoring systems.
Apply international standards and best practices in ESG-compliant mining operations.

Target Audience

Mineral Processing Engineers
Metallurgists and Extractive Metallurgy Professionals
Geologists and Exploration Specialists
Mining Engineers and Plant Operators
Laboratory Analysts and Mineralogists
Research Scientists and Academic Professionals
Environmental and Sustainability Officers in Mining
Graduate Students and Technical Consultants in Mining & Minerals

Course Modules

Module 1: Fundamentals of Mineral Liberation Analysis

Introduction to MLA principles and workflows
Mineral liberation concepts and particle characterization
Overview of SEM and automated mineralogy systems
Mineral identification and classification techniques
Data acquisition and interpretation fundamentals
Case Study: Optimization of copper ore liberation analysis for improved flotation recovery in a large-scale concentrator plant.

Module 2: Automated Mineralogy and SEM Applications

SEM instrumentation and operational procedures
Backscattered electron imaging techniques
Energy dispersive spectroscopy (EDS) analysis
Automated particle analysis workflows
Calibration and quality control procedures
Case Study: Gold ore mineralogical mapping using SEM-EDS systems for enhanced recovery prediction.

Module 3: Quantitative Mineralogical Analysis

Quantitative phase analysis methods
Mineral abundance and grain size distribution
Liberation and association measurements
Data validation and statistical interpretation
Reporting standards for mineralogical studies
Case Study: Iron ore beneficiation optimization through quantitative mineralogical analysis.

Module 4: Geometallurgy and Ore Characterization

Geometallurgical modeling principles
Ore variability and spatial mineral distribution
Integration of geological and processing data
Predictive mineral processing models
Resource-to-recovery optimization strategies
Case Study: Geometallurgical modeling of polymetallic deposits for production forecasting.

Module 5: Process Mineralogy and Plant Optimization

Process mineralogy in comminution circuits
Flotation optimization using MLA data
Gravity and magnetic separation analysis
Hydrometallurgical process support
Process troubleshooting and optimization techniques
Case Study: Flotation circuit optimization in a platinum processing plant using MLA datasets.

Module 6: Critical Minerals and Battery Materials Analysis

Characterization of lithium-bearing minerals
Rare earth element mineralogy
Graphite and battery raw material analysis
Sustainability and strategic minerals processing
Advanced beneficiation techniques for critical minerals
Case Study: Lithium ore characterization project for battery-grade concentrate production.

Module 7: Digital Mining and Data Analytics

Industry 4.0 applications in mining
AI-driven mineral data interpretation
Machine learning for mineral classification
Digital twins in mineral processing plants
Big data analytics and process automation
Case Study: AI-assisted predictive maintenance and ore sorting in smart mining operations.

Module 8: Sustainable Mining and ESG Applications

Environmental impact assessment in mineral processing
Tailings characterization and reprocessing
Circular economy approaches in mining
ESG compliance and sustainability reporting
Energy-efficient mineral beneficiation technologies
Case Study: Tailings reprocessing project for resource recovery and environmental remediation.

Training Methodology

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Mineral characterization Liberation analysis Automated microscopy Particle analysis

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Nov 02 2026

02 Nov — 06 Nov 2026

💻 Online / Virtual session

Online $1,000