AI for Sustainable Urban Mining

About This Course

This 3-week  course shows you how Life Cycle Assessment (LCA) and Machine Learning (ML) can work together to recover critical minerals from e-waste. You’ll move from the fundamentals of urban mining to practical impact assessment, predictive modeling, and finally an integrated AI + LCA workflow—with hands-on activities in each module.

Aim

To train participants to apply LCA + ML for sustainable urban mining—so they can make smarter, data-driven decisions for critical mineral recovery, recycling efficiency, and circular economy outcomes.

Course Structure

Module 1 — Urban Mining & Life Cycle Thinking

• What urban mining really means (and why it matters globally)

• Critical minerals: what they are, where they hide in e-waste, and why they’re strategic

• LCA essentials: goal & scope, inventory, impact assessment, interpretation

• Circular material accounting: where tools like openLCA / SimaPro fit in

• Hands-on: Build a basic LCA for a common electronic product (mobile/laptop)

Module 2 — Machine Learning for Smarter E-Waste Recovery

• ML concepts you’ll actually use in e-waste recovery (classification, regression basics)

• Material classification & sorting: how ML supports separation decisions

• Predicting recovery potential of critical materials from mixed e-waste streams

• Real-world case examples: what ML can (and can’t) do in facilities

• Data realities: incomplete records, mixed sources, non-uniform inputs

• Hands-on: Train a simple ML model to predict recovery using an e-waste dataset

Module 3 — Integrated Intelligence for Sustainable Recovery

• How to combine LCA + ML for better decisions (trade-offs, scenario comparisons)

• AI-assisted optimization: facility design, routing/logistics, process selection

• Turning insights into action: dashboards for recovery efficiency + environmental impact

• Hands-on: Build a dashboard that merges LCA impact results with ML predictions

Who Should Enrol?

• Students & researchers in AI/ML, sustainability, materials science, environmental engineering

• Professionals in recycling, waste management, resource recovery

• Data scientists/engineers who want real sustainability use-cases for ML

• Policymakers/consultants working on e-waste, circular economy, critical minerals

• Entrepreneurs building solutions in urban mining and green tech