The Mintek-SCI Grad Hackathon 2026 is an innovation challenge that seeks groundbreaking, implementable solutions to real-world problems in South Africa's minerals, mining, and metallurgy sector. This is your opportunity to apply your science, engineering, and technology knowledge to issues that matter to the industry, from critical minerals traceability and green hydrogen integration to water management, battery recycling, and AI-driven pyrometallurgy optimisation.
Our Aims:
Ignite Innovation: Spark creativity and pioneering spirit.
Build Skills: Equip participants to develop impactful solutions.
Foster Collaboration: Encourage teamwork throughout the development and presentation phases.
Respect IP: Promote innovation while ensuring intellectual property rights are protected via contractual agreements managed by the Mintek Office of Technology Transfer (MOTT).
The hackathon is open to university students at both?the undergraduate and postgraduate levels. We especially encourage students studying in the following fields to apply:
Eligibility:
Resources:
Please note: Participating teams are required to utilise their own resources (laptops, necessary software, data sources, etc.) during the September development phase and bring necessary equipment (like laptops) to the final event on Oct 1-2.
The Challenges:
Choose one challenge area for your application. Selected teams will receive more specific instructions for their chosen area on September 1st.
Problem 1
Problem Setting:
South Africa is positioning itself as a global leader in the green hydrogen economy, particularly in regions like Limpopo. Hydrogen offers a zero-emission alternative to diesel for heavy-duty mining vehicles and industrial heating. However, integration into existing mine sites is hindered by high infrastructure costs and technical complexity. Many mines lack the intelligence to balance intermittent renewable energy supply with the steady demand required for hydrogen electrolysis.
Challenge:
Design an intelligent energy management system or technical concept that optimises the production and storage of green hydrogen using on-site renewable energy (solar/wind), schedules hydrogen usage for mining operations to maximise efficiency and minimise costs, and demonstrates a scalable path for transitioning a traditional mine site to a hydrogen-powered facility.
Deliverable:
A simulation model, technical concept, or software dashboard demonstrating optimal energy–hydrogen load balancing. Submissions must include:
Problem 2
Circular Economy: Valorisation of Electronic Waste (Urban Mining)
Problem Setting:
E-waste is one of the fastest-growing waste streams globally and contains significant concentrations of valuable and critical minerals like gold, copper, and palladium—often at higher grades than natural ore. Despite this, recycling rates remain low due to the complexity of manual sorting and the energy intensity of traditional smelting methods. Failure to recover these materials represents a lost economic opportunity and exacerbates the environmental impact of primary mining.
Challenge:
Develop an automated or low-energy solution that improves the identification and sorting of high-value components within e-waste, proposes a material science or chemical-based method for recovering minerals from secondary sources with minimal environmental impact, and supports a circular economy model by re-integrating recovered materials into the industrial supply chain.
Deliverable:
A technical prototype or process flow diagram for an e-waste recovery system. Submissions must include:
Problem 3
Computer Vision for Real-Time Mineralogical Characterisation
Problem Setting:
In mineral processing, understanding the mineralogical composition of the ore (phase identification) is critical for optimising recovery. Currently, this characterisation relies on slow, laboratory-based SEM or XRD analysis, which can take days. Without real-time feedback, processing plants cannot adjust to changes in ore quality, leading to inefficient chemical usage and lower mineral yields.
Challenge:
Develop a computer vision or machine learning algorithm that analyses high-resolution imagery or sensor data (e.g., hyperspectral) to identify mineral phases in real-time, predicts the "processability" of the ore based on its visual or spectral characteristics, and integrates with existing sorting or flotation controls to provide immediate operational feedback.
Deliverable:
A trained AI model or software tool capable of identifying at least three distinct mineral phases from provided image datasets. Submissions must include:
Problem 4
Using Mine-Impacted Water for Energy Generation
Problem Setting
Mine-impacted water (acidic, high in metals, or saline) is often treated solely as a waste problem. However, such water contains chemical potential that could be harnessed for energy generation, for example through flow batteries, redox systems using metal ions, or osmotic energy methods. This creates an opportunity to turn a pollution problem into an energy resource for mine operations or local communities.
Challenge:
Develop a concept or system design that uses mine-impacted water for energy generation, integrates the energy generation process with mine operations or community energy needs, and demonstrates a feasible path for scaling the technology.
Deliverable:
A technical concept, simulation model, or system design for energy generation from mine-impacted water. Submissions must include:
Problem 5
AI-Driven Optimisation of Pyrometallurgical Processes
Problem Setting
Pyrometallurgical processes (e.g. smelting, roasting, refining) are central to South Africa's minerals beneficiation and metal production, particularly for platinum group metals (PGMs), chrome, manganese, and base metals. These processes are energy-intensive, operate at extreme temperatures, and are highly sensitive to feed composition, operating conditions, and furnace dynamics. Current control strategies often rely on operator experience and reactive adjustments, leading to suboptimal energy use, inconsistent metal recovery, higher emissions, and shortened furnace life. Real-time optimisation is difficult due to complex, non-linear relationships between process variables and limited access to real-time compositional data.
Challenge:
Develop an AI-driven solution that optimises pyrometallurgical process performance by predicting key outcomes (e.g. metal recovery, energy consumption, slag composition, furnace temperature profile) from available process data and feed characteristics. The solution should enable proactive control decisions that improve energy efficiency, increase metal recovery, reduce emissions, and extend furnace campaign life.
Deliverable:
A functional AI model or decision-support tool for pyrometallurgical process optimisation. Submissions must include:
Step 1: Form Your Team
Gather 2–5 teammates who are passionate about solving minerals sector challenges.
Step 2: Get University Permission
Ensure you have formal approval from your university to participate in the hackathon.
Step 3: Submit Your Application
Complete the registration form and submit a?short abstract?(1–2 pages) describing:
Submission Deadline:?30 July 2026?
Ready to innovate and compete?
Start forming your teams!
All submissions will be rigorously evaluated based on the following:
Innovation & Creativity: Is the solution novel and visionary?
Technical Feasibility: Can the solution be implemented in a real-world minerals sector context?
Impact and Value: Does the solution address a significant industry challenge?
Originality: All submissions will undergo AI generation checks,Intellectual Property (IP) verification, and reference/originality checks to ensure the work is authentic and properly credited.
Clarity of Presentation: Effectiveness and clarity of the team's presentation and Q&A responses during the Oct 1-2 event.
Prizes and Opportunities
The Winning Team:
- Will receive a valuable cash prize
- Will be announced in November 2026
- If a solution demonstrates further potential for implementation or commercialisation,Mintek will offer vacation work opportunities for team members.
- This provides a pathway for students to gain real industry experience, work on live projects, and build their professional networks in the minerals sector.
Why Participate?
Download Mintek-SCi Grad Hackathon 2026 FAQ document
Download Mintek SCI and Grad Hackathon Privacy Policy document
Thank you for your interest in the Mintek-SCi Grad Hackathon!
Please complete this form accurately and submit it before the deadline: July 30, 2026. Ensure you have all required team member details and documents ready before you begin. Fields marked with * are required.