AI & LCA for Critical Minerals Recovery from E- Waste (Colab-First Edition)

Revolutionizing E-Waste: Leveraging AI and LCA for Sustainable Critical Mineral Recovery

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Advanced Dates
05 Dec 2025 to 08 Dec 2025 Program Length
1hr

Skills you will gain:

About Program:

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.

Program 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.

What you will learn?

📅 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

Mentor Profile

Gurpreet Assistant Professor

Fee Plan

INR 1999 /- OR USD 50

ACCESS THIS PROGRAM

Get an e-Certificate of Participation!

Intended For :

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.

Career Supporting Skills

Program 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)