📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI for Circular Manufacturing: Waste Reduction, Recycling & Waste-to-Energy, from foundations to a certified capstone project.
Outline
Define circular manufacturing principles and waste hierarchies • Analyse material flow using AI‑based tracking • Identify key performance indicators for waste reduction
Outline
Collect sensor and ERP data from production lines • Clean and normalise heterogeneous waste datasets • Engineer features for recycling and energy‑recovery models
Outline
Build regression models to forecast waste streams • Validate models with cross‑validation on industrial data • Deploy models for real‑time monitoring
Outline
Apply clustering to segment recyclable materials • Design decision‑support systems for route optimisation • Integrate reinforcement learning for adaptive sorting
Outline
Model calorific value using AI‑driven thermodynamic equations • Optimise feedstock mix for maximum energy yield • Simulate plant performance under varying load conditions
Outline
Containerise models with Docker for scalable rollout • Set up monitoring dashboards and alerting • Implement feedback loops for model retraining
Earn government-registered certification in AI for Circular Manufacturing: Waste Reduction, Recycling & Waste-to-Energy
e-Certificate and e-Marksheet issued on successful completion.
