📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI-Assisted Waste-to-Energy & Removal Modeling, from foundations to a certified capstone project.
Outline
Explore the waste‑to‑energy landscape (MSW, biomass, sludge, organics) • Define carbon‑negative logic (biogenic carbon, avoided emissions, credits) • Build a baseline LCA skeleton for a selected route and compute kg CO₂e/kWh
Outline
Apply AI tools to flag missing LCI data and generate smart assumptions • Automate unit conversion and create reusable scenario templates • Validate data quality and document uncertainty hotspots
Outline
Introduce bio‑char, CCS/BECCS, mineralisation, and digestate strategies • Model avoided burden (grid displacement, landfill diversion, fertilizer substitution) • Add a removal option to the Day‑1 model and run AI‑driven sensitivity sweeps
Outline
Prioritise key drivers (methane leakage, efficiency, transport, credit assumptions) • Use AI to auto‑generate parameter sets and rapid uncertainty screening • Interpret results to identify net‑negative operating windows
Outline
Calculate net GHG, energy yield, removal effectiveness, permanence risk, robustness score • Benchmark 2‑3 pathways (AD, gasification, pyrolysis) under identical assumptions • Generate AI‑assisted assumptions tables and anomaly flags
Outline
Create stakeholder‑ready dashboards with boundaries disclosure • Draft claim statements with guardrails, dos & don’ts, and uncertainty notes • Produce a simple MRV‑style template for ongoing verification
Earn government-registered certification in AI-Assisted Waste-to-Energy & Removal Modeling
e-Certificate and e-Marksheet issued on successful completion.
