Rated Excellent
250+ Courses
30,000+ Learners
95+ Countries
USD $0.00
Cart
Sale!
AI & LCA for Critical Minerals Recovery from E- Waste (Colab-First Edition)
Original price was: USD $99.00.USD $59.00Current price is: USD $59.00.
Revolutionizing E-Waste: Leveraging AI and LCA for Sustainable Critical Mineral Recovery
About This Course
This course focuses on the use of AI and Life Cycle Assessment (LCA) to enhance the sustainable recovery of critical minerals from e-waste. Participants will learn how AI can optimize recovery processes and how LCA can assess environmental impacts, aiming to improve e-waste management and support a circular economy. Through hands-on experience, you’ll gain the tools to integrate AI and LCA for more efficient, sustainable, and environmentally friendly mineral recovery.
Aim
The goal of this course is to explore how AI and LCA can be integrated to optimize the recovery of critical minerals from e-waste, with a focus on improving efficiency, sustainability, and reducing environmental impact throughout the recovery process.
Course Structure
📅 Module 1 – Urban Mining Foundations & Simplified LCA
- Introduction to Urban Mining and Critical Minerals: Learn about critical minerals like Au, Ag, Pd, Cu, Co, Li, Ni, and REEs, and their importance in e-waste recycling.
- Life Cycle Thinking for E-Waste Streams: Understand how life cycle thinking is applied to e-waste recovery processes.
- Simplified LCA Methodology: Learn to conduct simplified LCA without the need for paid databases, and focus on emission factor modeling and Bill-of-Materials (BoM) to inventory estimation.
- Hands-On Labs: Load BoM data, estimate critical metal content, run a simplified LCA using CSV factors, and generate device-wise KPI reports for metal mass, energy use, and GHG emissions.
- Tools: Pandas, NumPy, CSV templates.
📅 Module 2 – Machine Learning for Mineral Recovery Prediction
- Feature Engineering from E-Waste Device Attributes: Learn how to extract features from e-waste device attributes to predict mineral recovery potential.
- Label Estimation: Understand how to estimate metal content, such as gold (e.g., mg/device), for prediction models.
- Regression & Classification Models for Recovery Potential: Build and train machine learning models for predicting mineral recovery potential, including regression and classification techniques.
- Hands-On Labs: Train an ML model for gold recovery, classify devices based on their gold content, and save model artifacts.
- Tools: Scikit-learn, Joblib, Matplotlib.
📅 Module 3 – Integrating AI & LCA in a Dashboard
- Linking ML Recovery Predictions with Environmental Impact: Explore how to combine mineral recovery predictions with environmental impact assessment, such as GHG emissions and energy use.
- Visualizing Trade-Offs: Learn how to visualize the trade-offs between GHG emissions, recovery, and revenue.
- Creating User-Friendly Dashboards: Build a Gradio-based dashboard that enables decision-making through live predictions on recovery, revenue, and GHG impact.
- Hands-On Labs: Create and customize interactive sliders for electricity, transport, and reagent impacts.
- Tools: Gradio, Plotly, Scikit-learn.
📅 Module 4 – Logistics, Techno-Economic Analysis & Pilot Packaging
- E-Waste Collection Optimization: Learn how to optimize e-waste collection using the Vehicle Routing Problem (VRP) model.
- Line Balancing and Throughput Modeling for Recovery Plants: Study how to model throughput in recovery plants for efficiency.
- Techno-Economic Analysis (TEA): Understand the financial aspects of recovery processes, including costs and revenue generation.
- Hands-On Labs: Solve the routing problem with OR-Tools, model throughput, estimate recovery revenue, and calculate costs.
- Tools: OR-Tools, Folium, PuLP, Plotly, Pandas.
Who Should Enrol?
- This course is perfect for students, PhD scholars, academicians, and industry professionals in environmental science, engineering, or e-waste management. If you have a basic understanding of Life Cycle Assessment (LCA), machine learning (ML), and data analysis techniques, this course is for you.
- Familiarity with Python programming and data manipulation tools like Pandas and NumPy is recommended, but not mandatory.
- Anyone interested in sustainable mining practices, e-waste management, and the recovery of critical minerals is encouraged to attend.
Course Outcomes
- Proficiency in AI/ML Techniques: Learn how to apply AI and ML algorithms to predict material properties in semiconductor research.
- Hands-on Experience: Gain practical experience in data extraction, model development, and machine learning workflows.
- Understanding Advanced AI Tools: Become familiar with cutting-edge deep learning models like CGCNN, MEGNet, and ALIGNN used in semiconductor material discovery.
- Enhanced Research Skills: Develop the ability to evaluate and interpret ML models for material property predictions.
- Career Readiness: Gain the knowledge and skills needed to pursue a career in materials science, AI research, or semiconductor technology.
Related products
-
AI for Environmental Sustainability Course
USD $59.00 USD $249.00Price range: USD $59.00 through USD $249.00 Select options This product has multiple variants. The options may be chosen on the product page -
Python for Data Science Course
USD $59.00 USD $249.00Price range: USD $59.00 through USD $249.00 Select options This product has multiple variants. The options may be chosen on the product page -
AI for Fraud Detection in BFSI: Navigating Financial Integrity Course
USD $59.00 USD $249.00Price range: USD $59.00 through USD $249.00 Select options This product has multiple variants. The options may be chosen on the product page -
Tableau for Business Intelligence Course
USD $59.00 USD $249.00Price range: USD $59.00 through USD $249.00 Select options This product has multiple variants. The options may be chosen on the product page -
AI-Driven Cybersecurity Course
USD $59.00 USD $249.00Price range: USD $59.00 through USD $249.00 Select options This product has multiple variants. The options may be chosen on the product page
Reviews
There are no reviews yet.