AI Anaerobic Digestion Control

The Problem

“Stabilize biogas yield amid volatile digester conditions”

Organizations face these key challenges:

Feedstock variability (COD/VS, fats/oils/grease, inhibitors) causes unpredictable gas yield and sudden VFA accumulation

Limited real-time visibility (lab results delayed by hours/days) leads to conservative operation and late response to instability

Gas quality and reliability requirements (CH4%, H2S, siloxanes, moisture) create penalties, curtailment, or equipment damage when control is reactive

Impact When Solved

Increase methane production 3–8% while maintaining stability constraintsCut unplanned downtime and upset-related curtailment 15–30% through early warning and predictive controlReduce chemical dosing and energy use 5–15% by optimizing alkalinity, nutrients, mixing, and temperature setpoints

The Shift

Real-World Use Cases

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5

AI Grid Congestion Management

This AI helps optimize the layout of power grids to reduce congestion without increasing costs or carbon emissions.

optimizationgrowing

8.0