Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI & LCA for Critical Minerals Recovery from E-Waste tackles a strategic sustainability problem: reclaiming the critical minerals locked in mountains of electronic waste. You learn how AI improves recovery — sorting and characterising e-waste, optimising recovery processes — and how life-cycle assessment quantifies the environmental case for recovery versus mining. The course connects recovery technology to critical-material security and the circular economy. You finish able to reason about an AI-and-LCA approach to e-waste mineral recovery. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI and life-cycle assessment for critical-mineral recovery from e-waste — recovering valuable minerals from electronic waste sustainably and efficiently.
📋 Course Objectives
1. Characterise and sort e-waste with AI.
2. Optimise critical-mineral recovery processes.
3. Assess recovery impact with life-cycle assessment.
4. Compare recovery against primary mining.
5. Connect recovery to material security.
👥 Who Should Enroll?
• Recycling and materials engineers
• Sustainability and LCA analysts
• Circular-economy professionals
• Students of resource recovery
🚀 Key Learning Outcomes
• An understanding of AI/LCA e-waste recovery.
• A critical-materials perspective.
• A circular-economy project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Foundations
Develop a comprehensive understanding of artificial neural networks and their applications in critical minerals recovery • Analyze the mathematical foundations of machine learning, including linear algebra and calculus, to optimize AI model performance • Design and implement basic AI models using Python and relevant libraries to solve problems in e-waste management
Data Engineering, Preprocessing, and Feature Pipelines
Configure and manage large datasets related to e-waste and critical minerals using data engineering techniques and tools • Evaluate and preprocess data to ensure quality and relevance for AI model training, including handling missing values and outliers • Implement feature engineering techniques to extract relevant features from datasets and improve AI model performance
Model Architecture, Algorithm Design, and Methods
Design and implement deep learning models, including convolutional neural networks and recurrent neural networks, for critical minerals recovery • Analyze and compare the performance of different AI algorithms, including supervised and unsupervised learning methods, for e-waste management • Develop and optimize AI model architectures using techniques such as transfer learning and ensemble methods
Training, Hyperparameter Optimization, and Evaluation
Train and evaluate AI models using various metrics, including accuracy, precision, and recall, to ensure optimal performance • Implement hyperparameter optimization techniques, including grid search and random search, to improve AI model performance • Configure and use cross-validation methods to evaluate AI model performance and prevent overfitting
Deployment, MLOps, and Production Workflows
Deploy AI models in production environments using containerization and orchestration tools, such as Docker and Kubernetes • Design and implement MLOps pipelines to automate AI model training, deployment, and monitoring • Configure and use continuous integration and continuous deployment (CI/CD) tools to streamline AI model development and deployment
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and mitigate bias in AI models using techniques such as data preprocessing and regularization • Develop and implement responsible AI practices, including transparency, explainability, and accountability • Evaluate the ethical implications of AI model deployment and use in critical minerals recovery and e-waste management
Industry Integration, Business Applications, and Case Studies
Develop business cases and applications for AI in critical minerals recovery and e-waste management • Analyze and evaluate the economic and environmental benefits of AI adoption in the industry • Implement AI solutions in real-world industry settings, including integration with existing systems and processes
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | Docker |
| Covered Tool / Platform | Kubernetes |
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