AI Carbon Capture Process Optimization

Uses AI to optimize solvent/regeneration conditions, energy use, and capture rates while maintaining CO2 purity and operability.

The Problem

“Optimize carbon capture performance under real-time variability”

Organizations face these key challenges:

High and variable energy penalty (steam/electricity) driven by load cycling, ambient conditions, and suboptimal setpoints

Solvent degradation, foaming, and contamination causing capture-rate drift, corrosion risk, and increased make-up cost

Limited observability and slow feedback loops (lab tests, intermittent analyzers) leading to delayed detection of breakthrough and off-spec CO2

Impact When Solved

Reduce reboiler duty 5–15% while maintaining ≥90% capture rate under variable loadsCut solvent make-up and emissions 10–25% via predictive control of degradation/foaming conditionsImprove unit availability 0.5–2.0% by early detection of fouling, flooding risk, and equipment performance loss

The Shift

Real-World Use Cases

Artificial Intelligence for Energy Systems

Think of this as a playbook of AI tricks for running power systems—generation, grids, and consumption—more like a smart thermostat and less like a manual on/off switch. It applies machine learning to decide how much power to produce, when to store it, and how to route it so the overall system is cheaper, cleaner, and more reliable.

Time-SeriesEmerging Standard

8.5

Federated Carbon Intelligence for Sustainable AI Optimization

Imagine your company runs many different types of computers and AI chips in data centers around the world. This system is like a smart air-traffic controller that constantly checks which machines are cleanest (lowest carbon), cheapest, and most efficient right now, then routes AI workloads to the best place in real time—without each site having to share sensitive internal data.