Introduction

In today’s competitive mining and minerals industry, organizations are increasingly relying on AI-enabled process optimization, predictive metallurgy, multivariate experimentation, and sustainable beneficiation strategies to maximize yield while reducing operational costs and environmental impact. Experimental Design in Mineral Processing Training Course bridges the gap between theoretical statistical methods and real-world mineral processing applications, enabling professionals to design robust experiments that improve flotation efficiency, grinding performance, leaching kinetics, and separation processes.

Participants will gain hands-on expertise in factorial design, response surface methodology (RSM), Taguchi techniques, and machine learning-assisted experimental planning. The training emphasizes practical implementation in concentrators, metallurgical plants, and pilot-scale operations. By integrating process analytics, simulation tools, and real plant case studies, the course ensures participants develop the capability to optimize complex mineral systems under uncertainty, variability, and industrial constraints. This makes it a critical upskilling program for modern mining operations focused on digital transformation, operational excellence, and sustainable mineral beneficiation.

Programme Curriculum

Experimental Design in Mineral Processing Training Course

Introduction

Course Duration

5 days

Course Objectives

Master Design of Experiments (DoE) in Mineral Processing
Apply Response Surface Methodology (RSM) for process optimization
Develop skills in statistical process optimization in mining
Implement factorial and fractional factorial experimental designs
Optimize flotation recovery and grade using experimental models
Enhance grinding circuit performance through DoE techniques
Analyze mineral processing data using ANOVA and regression modeling
Apply Taguchi robust design methods in metallurgical processes
Integrate machine learning with experimental design frameworks
Improve leaching efficiency and reagent optimization
Conduct pilot plant experimental design and scale-up analysis
Minimize operational costs through process optimization strategies
Build capability in data-driven mineral beneficiation decision-making

Target Audience

Mineral Processing Engineers
Metallurgical Engineers
Mining Process Engineers
Plant Operations Managers
Research & Development Scientists
Geometallurgists
Data Analysts in Mining Industry
Graduate Students in Extractive Metallurgy

Course Modules

Module 1: Fundamentals of Experimental Design in Mineral Processing

Principles of DoE and industrial applications
Variables, responses, and experimental factors
Randomization, replication, blocking techniques
Introduction to mineral processing variability
Statistical thinking in metallurgy
Case Study: Optimization of ore grind size distribution in a gold processing plant using basic factorial design.

Module 2: Factorial and Fractional Factorial Designs

Full factorial experimental setup
Fractional factorial reduction techniques
Interaction effects in mineral systems
Screening of process variables
Cost-effective experimentation strategies
Case Study: Reduction of reagent usage in a copper flotation circuit using fractional factorial design.

Module 3: Response Surface Methodology (RSM)

Central composite design (CCD)
Box-Behnken design applications
Quadratic modelling of mineral processes
Optimization of multi-variable systems
Surface contour interpretation
Case Study: Maximizing iron ore recovery using RSM-based flotation parameter optimization.

Module 4: Statistical Analysis & ANOVA Techniques

Hypothesis testing in mineral experiments
Analysis of variance (ANOVA)
Regression diagnostics and model validation
Residual analysis in metallurgical data
Data interpretation for decision-making
Case Study: Statistical validation of leaching efficiency improvement in a uranium processing plant.

Module 5: Taguchi Methods & Robust Design

Signal-to-noise ratio concepts
Orthogonal arrays in mineral testing
Robust parameter design
Noise factor identification
Process stability enhancement
Case Study: Stabilizing recovery rates in a platinum group metals (PGM) flotation circuit.

Module 6: Process Optimization in Grinding & Comminution

Ball mill and SAG mill optimization
Energy efficiency modeling
Particle size distribution analysis
Throughput improvement strategies
Wear and liner optimization
Case Study: Energy reduction in a copper grinding circuit using experimental optimization techniques.

Module 7: Flotation & Hydrometallurgical Process Design

Reagent optimization strategies
pH and chemical conditioning effects
Bubble-particle interaction modeling
Leaching kinetics experimentation
Separation efficiency improvement
Case Study: Improving lithium recovery through optimized flotation reagent design.

Module 8: Advanced Data Analytics & AI Integration in DoE

Machine learning in experimental design
Predictive modeling for mineral processing
Digital twins in metallurgical plants
Multivariate optimization techniques
Big data integration in mining operations
Case Study: AI-assisted optimization of a platinum concentrator plant throughput and recovery.

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

Design of experiments Process optimization Data analysis Statistical modeling

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