AI & LCA for Critical Minerals Recovery from E- Waste (Colab-First Edition)
Revolutionizing E-Waste: Leveraging AI and LCA for Sustainable Critical Mineral Recovery
About This Course
This workshop focuses on using AI and Life Cycle Assessment (LCA) to enhance the sustainable recovery of critical minerals from e-waste. Participants will learn how AI optimizes recovery processes and how LCA evaluates environmental impacts, aiming to improve e-waste management and support a circular economy.
Aim
The aim of this workshop is to explore the integration of AI and LCA to optimize critical mineral recovery from e-waste, focusing on enhancing efficiency, sustainability, and environmental impact reduction throughout the recovery process.
Application Details
Build Life Cycle Assessments (LCA) for products, processes, or systems.
Let you enter and manage material, energy, and process data.
Use AI to suggest processes/materials and help fill data gaps.
Calculate environmental impacts (e.g., carbon, energy, water).
Compare scenarios (baseline vs. alternatives) and visualize results for decision-making.
Workshop Objectives
- To equip participants with the knowledge and tools to integrate AI and Life Cycle Assessment (LCA) for sustainable critical mineral recovery from e-waste.
- To develop practical skills in using machine learning for predicting mineral recovery potential and integrating it with environmental impact analysis.
- To provide hands-on experience in creating AI-driven dashboards and optimizing e-waste recovery processes through real-time data visualization.
- To introduce participants to logistics and techno-economic analysis for efficient e-waste management and recovery plant optimization.
Workshop Structure
📅 Day 1 – Urban Mining Foundations & Simplified LCA
- Introduction to Urban Mining and Critical Minerals (Au, Ag, Pd, Cu, Co, Li, Ni, REEs)
- Life Cycle Thinking for E-Waste Streams
- Simplified LCA Methodology (no paid databases)
- Emission factor modeling and BoM-to-inventory estimation
- Hands-On Labs (Colab): Load BoM data, estimate critical metal content, run simplified LCA via CSV factors
- Export device-wise KPI reports (metal mass, energy use, GHG emissions)
- Tools: Pandas, NumPy, CSV templates
📅 Day 2 – Machine Learning for Mineral Recovery Prediction
- Feature engineering from e-waste device attributes
- Label estimation (e.g., Au mg/device)
- Regression & classification models for recovery potential
- Hands-On Labs (Colab): Train ML model for gold recovery, classify high-gold devices
- Save model artifacts (joblib) and generate model cards
- Tools: Scikit-learn, Joblib, Matplotlib
📅 Day 3 – Integrating AI & LCA in a Dashboard
- Linking ML recovery predictions with environmental impact
- Visualizing trade-offs between GHG emissions and revenue
- Creating user-friendly dashboards for decision-making
- Hands-On Labs (Colab): Build Gradio-based dashboard with live predictions of recovery, revenue, and GHG impact
- Customize sliders for electricity, transport, and reagent impact
- Tools: Gradio, Plotly, Scikit-learn
📅 Day 4 – Logistics, Techno-Economic Analysis & Pilot Packaging
- E-Waste collection optimization (Vehicle Routing Problem)
- Line balancing and throughput modeling for recovery plants
- Techno-Economic Analysis (TEA) of recovery processes
- Packaging industrial pilot solutions
- Hands-On Labs (Colab): Solve routing problem with OR-Tools, model throughput, estimate recovery revenue & costs
- Export all files into a Pilot Pack (ZIP)
- Tools: OR-Tools, Folium, PuLP, Plotly, Pandas
Who Should Enrol?
- Students, PhD Scholars, Academicians, and Industry Professionals in the fields of environmental science, engineering, or e-waste management.
- Participants with a basic understanding of Life Cycle Assessment (LCA), Machine Learning (ML), and data analysis techniques are preferred.
- Familiarity with Python programming and data manipulation tools (e.g., Pandas, NumPy) is recommended but not mandatory.
- Anyone interested in sustainable mining practices, e-waste management, and critical mineral recovery is encouraged to attend.
Important Dates
Registration Ends
12/05/2025
IST 4:30 PM
Workshop Dates
12/05/2025 – 12/08/2025
IST 5:30
Workshop Outcomes
- Device-wise metal content + impact tables (Day 1)
- Trained ML models + model card (Day 2)
- Streamlit or Gradio dashboard (Day 3)
- VRP route maps, TEA snapshot, Pilot Pack ZIP (Day 4)
Fee Structure
Student
₹2999 | $90
Ph.D. Scholar / Researcher
₹3999 | $110
Academician / Faculty
₹4999 | $130
Industry Professional
₹6999 | $151
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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