AI-Enabled Waste-to-Energy

Course Overview

Home >Courses >AI-Enabled Waste-to-Energy

Program Syllabus

Module 1

About This Course

This 3-day hands-on workshop teaches how to assess WtE routes (e.g., anaerobic digestion, gasification, pyrolysis) through an LCA lens and layer AI to accelerate inventory building, harmonize units, detect data gaps, and rapidly test “carbon-negative” conditions. Participants will integrate carbon removal options (biochar, CCS/BECCS, mineralization, digestate strategies), run sensitivity sweeps to identify key drivers, and produce stakeholder-ready dashboards and claim statements with transparent boundaries and uncertainty notes.

Module 2

Aim

To train participants to design, evaluate, and report waste-to-energy pathways using AI-assisted LCA, carbon-removal integration, sensitivity/uncertainty screening, and MRV-style reporting for defensible carbon-negative claims.

Module 3

Workshop Objectives

Understand WtE pathways and the logic behind carbon-negative claims (biogenic carbon, avoided emissions, credits, permanence).
Build LCA models with correct functional units, boundaries, allocation, and data-quality checks.
Use AI to identify missing LCI data, standardize units, and generate scenario templates.
Integrate carbon-removal options and apply system expansion/avoided burden correctly.
Run AI-assisted sensitivity and uncertainty screening to identify net-negative conditions and main risk drivers.
Create reporting-ready outputs: assumptions table, dashboard, claim guardrails, and MRV-style templates.

Module 4

Workshop Structure

Module 5

📅 Day 1 — WtE Pathways + AI-Ready LCA Framing (Carbon-Negative Claims)

Waste-to-energy landscape: MSW, biomass residues, sludge, industrial organics
Carbon-negative logic: biogenic carbon, avoided emissions, displacement credits, permanence
LCA essentials for WtE: functional unit, system boundaries, allocation, data quality, uncertainty hotspots
AI layer: LCI data gap detection, smart assumptions, automated unit harmonization, quick scenario templates
Hands-on: Build a baseline LCA skeleton for one WtE route (e.g., anaerobic digestion) and compute kg CO₂e/kWh and/or kg CO₂e/ton waste + an AI-assisted checklist of missing inventory data

Module 6

📅 Day 2 — AI-Assisted Carbon Removal Integration + System Expansion

Carbon removal routes in WtE: biochar, CCS/BECCS, mineralization, digestate management
Modeling avoided burden: grid displacement, landfill diversion, fertilizer substitution
AI layer: sensitivity auto-generation, parameter prioritization (what drives net-negative), rapid uncertainty screening
Key sensitivity drivers: methane leakage, energy efficiency, transport distances, credit assumptions, permanence risk
Hands-on: Add one carbon removal option to the Day-1 model and run an AI-assisted sensitivity sweep to identify conditions for carbon-negative operation

Module 7

📅 Day 3 — Decision Metrics + Digital MRV + Reporting-Ready Carbon-Negative Pathways

Decision metrics: net GHG, energy yield, removal effectiveness, permanence risk, robustness score
Scenario benchmarking: compare 2–3 pathways (AD vs gasification vs pyrolysis) under the same rules
AI layer: auto-generated assumptions table, claim guardrails, anomaly flags, and stakeholder-ready narratives
Reporting-ready outputs: results dashboard, boundaries disclosure, uncertainty notes, claim wording dos/don’ts
Hands-on: Generate a scenario comparison dashboard + finalize a carbon-negative claim statement with transparent boundaries, sensitivity notes, and a simple MRV-style reporting template

Module 8

Who Should Enrol?

Students, researchers, consultants, and professionals in sustainability, energy, waste management, environmental engineering, circular economy, or climate analytics.
Basic understanding of carbon accounting or LCA helps, but not required.
Comfortable with spreadsheets; light Python familiarity is useful (templates provided).

Module 9

Important Dates

Module 10

Registration Ends

02/04/2026
IST 4 : 00 PM

Module 11

Workshop Dates

02/04/2026 – 02/06/2026
IST 5 :30 PM

Module 12

Workshop Outcomes

Build a baseline LCA skeleton for a WtE route and compute kg CO₂e/kWh and/or kg CO₂e/ton waste.
Add a carbon removal option and determine conditions for carbon-negative operation via sensitivity sweeps.
Identify and rank key drivers (e.g., methane leakage, efficiency, transport, credits, permanence risk).
Benchmark 2–3 pathways under consistent rules and generate a comparison dashboard.
Draft a transparent carbon-negative claim statement with boundaries, assumptions, and uncertainty notes.
Produce a simple MRV-style reporting template suitable for stakeholders and audits.

Module 13

Fee Structure

Module 14

Student

₹2499 | $65

Module 15

Ph.D. Scholar / Researcher

₹3499 | $75

Module 16

Academician / Faculty

₹4499 | $85

Module 17

Industry Professional

₹6499 | $110

Module 18

What You’ll Gain

Live & recorded sessions
e-Certificate upon completion
Post-workshop query support
Hands-on learning experience

Module 19

Join Our Hall of Fame!

Take your research to the next level with NanoSchool.

Module 20

Publication Opportunity

Get published in a prestigious open-access journal.

Module 21

Centre of Excellence

Become part of an elite research community.

Module 22

Networking & Learning

Connect with global researchers and mentors.

Module 23

Global Recognition

Worth ₹20,000 / $1,000 in academic value.

Module 24

Need Help?

We’re here for you!

(+91) 120-4781-217

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Yujia Wu • 07/01/2024 at 8:31 pm

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Prediction of Protein Structure Using AlphaFold: An Artificial Intelligence (AI) Program

Good

Liz Maria Luke • 07/04/2024 at 8:16 pm

★★★★★

Large Language Models (LLMs) and Generative AI

The mentor was supportive, clear in their guidance, and encouraged active participation throughout the process.

António Ricardo de Bastos Teixeira • 07/03/2025 at 10:04 pm

View All Feedbacks →

Course Overview

Home >Courses >AI-Enabled Waste-to-Energy

Program Syllabus

Module 1

About This Course

Module 2

Aim

Module 3

Workshop Objectives

Understand WtE pathways and the logic behind carbon-negative claims (biogenic carbon, avoided emissions, credits, permanence).
Build LCA models with correct functional units, boundaries, allocation, and data-quality checks.
Use AI to identify missing LCI data, standardize units, and generate scenario templates.
Integrate carbon-removal options and apply system expansion/avoided burden correctly.
Run AI-assisted sensitivity and uncertainty screening to identify net-negative conditions and main risk drivers.
Create reporting-ready outputs: assumptions table, dashboard, claim guardrails, and MRV-style templates.

Module 4

Workshop Structure

Module 5

📅 Day 1 — WtE Pathways + AI-Ready LCA Framing (Carbon-Negative Claims)

Waste-to-energy landscape: MSW, biomass residues, sludge, industrial organics
Carbon-negative logic: biogenic carbon, avoided emissions, displacement credits, permanence
LCA essentials for WtE: functional unit, system boundaries, allocation, data quality, uncertainty hotspots
AI layer: LCI data gap detection, smart assumptions, automated unit harmonization, quick scenario templates
Hands-on: Build a baseline LCA skeleton for one WtE route (e.g., anaerobic digestion) and compute kg CO₂e/kWh and/or kg CO₂e/ton waste + an AI-assisted checklist of missing inventory data

Module 6

📅 Day 2 — AI-Assisted Carbon Removal Integration + System Expansion

Carbon removal routes in WtE: biochar, CCS/BECCS, mineralization, digestate management
Modeling avoided burden: grid displacement, landfill diversion, fertilizer substitution
AI layer: sensitivity auto-generation, parameter prioritization (what drives net-negative), rapid uncertainty screening
Key sensitivity drivers: methane leakage, energy efficiency, transport distances, credit assumptions, permanence risk
Hands-on: Add one carbon removal option to the Day-1 model and run an AI-assisted sensitivity sweep to identify conditions for carbon-negative operation

Module 7

📅 Day 3 — Decision Metrics + Digital MRV + Reporting-Ready Carbon-Negative Pathways

Decision metrics: net GHG, energy yield, removal effectiveness, permanence risk, robustness score
Scenario benchmarking: compare 2–3 pathways (AD vs gasification vs pyrolysis) under the same rules
AI layer: auto-generated assumptions table, claim guardrails, anomaly flags, and stakeholder-ready narratives
Reporting-ready outputs: results dashboard, boundaries disclosure, uncertainty notes, claim wording dos/don’ts
Hands-on: Generate a scenario comparison dashboard + finalize a carbon-negative claim statement with transparent boundaries, sensitivity notes, and a simple MRV-style reporting template

Module 8

Who Should Enrol?

Students, researchers, consultants, and professionals in sustainability, energy, waste management, environmental engineering, circular economy, or climate analytics.
Basic understanding of carbon accounting or LCA helps, but not required.
Comfortable with spreadsheets; light Python familiarity is useful (templates provided).

Module 9

Important Dates

Module 10

Registration Ends

02/04/2026
IST 4 : 00 PM

Module 11

Workshop Dates

02/04/2026 – 02/06/2026
IST 5 :30 PM

Module 12

Workshop Outcomes

Build a baseline LCA skeleton for a WtE route and compute kg CO₂e/kWh and/or kg CO₂e/ton waste.
Add a carbon removal option and determine conditions for carbon-negative operation via sensitivity sweeps.
Identify and rank key drivers (e.g., methane leakage, efficiency, transport, credits, permanence risk).
Benchmark 2–3 pathways under consistent rules and generate a comparison dashboard.
Draft a transparent carbon-negative claim statement with boundaries, assumptions, and uncertainty notes.
Produce a simple MRV-style reporting template suitable for stakeholders and audits.

Module 13

Fee Structure

Module 14

Student

₹2499 | $65

Module 15

Ph.D. Scholar / Researcher

₹3499 | $75

Module 16

Academician / Faculty

₹4499 | $85

Module 17

Industry Professional

₹6499 | $110

Module 18

What You’ll Gain

Live & recorded sessions
e-Certificate upon completion
Post-workshop query support
Hands-on learning experience

Module 19

Join Our Hall of Fame!

Take your research to the next level with NanoSchool.

Module 20

Publication Opportunity

Get published in a prestigious open-access journal.

Module 21

Centre of Excellence

Become part of an elite research community.

Module 22

Networking & Learning

Connect with global researchers and mentors.

Module 23

Global Recognition

Worth ₹20,000 / $1,000 in academic value.

Module 24

Need Help?

We’re here for you!

(+91) 120-4781-217

★★★★★

AI-Driven Design of Smart Polymer Composites: From Concept to Manufacturing

Well presented.

Daniel Argilashki • 10/28/2025 at 12:28 pm

★★★★★

Prediction of Protein Structure Using AlphaFold: An Artificial Intelligence (AI) Program

Thanks for the very attractive topics and excellent lectures. I think it would be better to include more application examples/software.

Yujia Wu • 07/01/2024 at 8:31 pm

★★★★★

Prediction of Protein Structure Using AlphaFold: An Artificial Intelligence (AI) Program

Good

Liz Maria Luke • 07/04/2024 at 8:16 pm

★★★★★

Large Language Models (LLMs) and Generative AI

The mentor was supportive, clear in their guidance, and encouraged active participation throughout the process.

António Ricardo de Bastos Teixeira • 07/03/2025 at 10:04 pm

View All Feedbacks →

Enrol Now
Explore

AI-Enabled Waste-to-Energy

Course Overview

Program Syllabus

About This Course

Aim

Workshop Objectives

Workshop Structure

📅 Day 1 — WtE Pathways + AI-Ready LCA Framing (Carbon-Negative Claims)

📅 Day 2 — AI-Assisted Carbon Removal Integration + System Expansion

📅 Day 3 — Decision Metrics + Digital MRV + Reporting-Ready Carbon-Negative Pathways

Who Should Enrol?

Important Dates

Registration Ends

Workshop Dates

Workshop Outcomes

Fee Structure

Student

Ph.D. Scholar / Researcher

Academician / Faculty

Industry Professional

What You&#8217;ll Gain

Join Our Hall of Fame!

Publication Opportunity

Centre of Excellence

Networking &#038; Learning

Global Recognition

Need Help?

Lead Instructor

Dr. Sarah Chen