AI Biomass Conversion Modeling
Uses machine learning to model biomass conversion processes (e.g., pyrolysis/gasification) and predict yields, emissions, and product quality.
The Problem
“Uncertain biomass conversion yields drive costly decisions”
Organizations face these key challenges:
Feedstock variability (moisture, ash, lignin/cellulose ratio, contaminants) causes unpredictable yields, emissions, slagging/fouling, and O&M cost swings
Slow, expensive experimentation and limited pilot data make it hard to confidently scale from lab to commercial operation and to compare pathways (AD vs gasification vs pyrolysis)
Manual scenario planning and static models cannot keep up with changing supply, equipment degradation, and tightening emissions/carbon-intensity requirements
Impact When Solved
Real-World Use Cases
Artificial Intelligence in Renewable Energy Optimization
This is like giving a wind farm or solar plant a very smart autopilot. It studies weather, demand, prices, and equipment behavior, then constantly tweaks how the system runs so you get more clean energy for less money and wear-and-tear.
AI-Driven Optimization for Hydrogen Production
We use smart computers to help make hydrogen energy more efficiently and reliably.