AI-Driven Green Ammonia: Electrolyzer Pathways, Storage Logistics, Carbon Intensity and LCOA

To give participants a practical, end-to-end understanding of green ammonia as fuel and fertilizer—covering electrolytic production, safe storage and logistics, and LCOA/CI modeling for bankable project decisions.

• Understand key electrolytic pathways (PEM/alkaline/SOEC) and their integration with ASU and Haber–Bosch/e-Haber.

• Size core units (electrolyzers, ASU, synthesis loop, storage) and develop first-cut LCOA estimates.

• Compare storage, transport, and end-use options for ammonia as fuel and fertilizer.

• Quantify carbon intensity (CI) and identify main technical and commercial LCOA/CI drivers.

• Build and test a decision-ready LCOA/CI model to support bankable green ammonia projects.

📅 Day 1 – Digital Foundations & Electrolyzer Pathways

• Digital overview: RE power → PEM/Alkaline/SOEC electrolysis → N₂ from ASU → Haber–Bosch/e-Haber
• Operational data layer: key process tags, historians/SCADA, data quality for AI/ML
• AI/ML for electrolyzers: specific energy, stack health, availability, anomaly detection
• Safety & process monitoring: loop P–T, purge/recycle, NH₃ toxicity, leak detection, area classification
• Hands-on: Build plant-sizing + LCOA v1 worksheet and define minimal data set for AI models

📅 Day 2 – AI-Enhanced Storage, Transport and End Use

• Storage systems: pressurized vs refrigerated tanks, boil-off and turnaround with digital monitoring
• Logistics & routing: truck/rail/ship/pipeline, AI-assisted fleet sizing, scheduling and inventory control
• End-use as fuel: engines/turbines/fuel cells, NOₓ monitoring, AI-guided combustion and H₂ cracking
• End-use as fertilizer: conversion to urea/nitrates, agronomic efficiency, digital agriculture links
• Hands-on: Size hub storage and logistics (fuel hub vs fertilizer plant) and compare rule-based vs AI-optimized dispatch

📅 Day 3 – Carbon Intensity, AI-Driven LCOA and Bankability

• LCOA drivers: CAPEX/OPEX, utilization, power price, stack replacement, incentives and credits
• Carbon intensity: CI (kg CO₂e/kg NH₃), grid vs RE profiles, AI-based CI forecasting and scenario analysis
• Certification & MRV: guarantees of origin, digital MRV systems, AI for data validation and anomaly flags
• Commercial & risk view: offtake (fuel vs fertilizer), indexation, risk allocation, AI-supported risk assessment
• Hands-on: Build LCOA v2 with sensitivity and generate an AI-assisted one-page investment memo (CI range, LCOA range, key risks/mitigations)

• Professionals in renewable energy, hydrogen, ammonia, and fertilizer industries

• Chemical, mechanical, energy, and process engineers (plant design, operations, EPC)

• Project developers, consultants, and investors evaluating green ammonia projects

• Researchers and postgraduate students in energy systems, climate tech, and process engineering

• Policy, sustainability, and ESG professionals working on low-carbon fuels and fertilizers

• Understand electrolytic green ammonia production pathways and their safe integration with ASU, Haber–Bosch/e-Haber, and renewables.

• Evaluate storage and transport options and compare green ammonia end-uses as fuel (engines, turbines, fuel cells) and as fertilizer.

• Build and refine an Excel-based LCOA and carbon intensity (CI) model with key sensitivities (power price, CF, stack life, incentives).

• Assess project bankability and prepare a concise investment-style memo highlighting CI, LCOA range, and major risks/mitigations.