AI Biogas Production Optimization

The Problem

“Maximize biogas yield while reducing digester upsets”

Organizations face these key challenges:

Feedstock variability (TS/VS, C:N ratio, fats/proteins, inhibitors) drives unstable methane yield and unpredictable H2S and CO2 levels

Reactive operations: upsets are detected late (VFA accumulation, pH drops, foaming), causing flaring, derates, and prolonged recovery periods

Suboptimal setpoints and blending due to limited visibility into time-lagged process dynamics, leading to conservative loading rates and lost revenue

Impact When Solved

3–10% higher methane production through optimized blending and OLR recommendations10–30% fewer upset-related downtime events with early-warning risk forecasting (VFA/alkalinity, foaming, ammonia inhibition)5–15% O&M reduction via optimized chemical dosing, mixing/heating energy, and reduced emergency maintenance

The Shift

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.

Time-SeriesEmerging Standard

8.5

AI-Driven Optimization for Hydrogen Production

We use smart computers to help make hydrogen energy more efficiently and reliably.

optimizationemerging

7.0