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 operations with AI while coordinating geo-aware compute routing to reduce total emissions”
Organizations face these key challenges:
High regeneration energy demand and narrow operating windows
Tradeoffs between capture rate, purity, throughput, and equipment constraints
Feed gas composition variability causing unstable absorber/regenerator performance
Limited ability to optimize in real time using manual engineering workflows
Sparse or delayed lab measurements for solvent health and CO2 purity validation
Difficulty combining process data, utility data, weather, and market signals into one control strategy
Static workload placement that ignores regional electricity carbon intensity
Latency-sensitive AI services cannot be moved freely without violating response targets
Cooling efficiency varies by region and season, changing the true emissions impact of compute placement
Operators need auditable recommendations before allowing closed-loop automation
Impact When Solved
The Shift
Human Does
- •Review capture performance, steam use, and CO2 purity from operating reports and lab results
- •Manually adjust solvent circulation, reboiler duty, and temperature setpoints based on experience and test runs
- •Investigate signs of solvent degradation, foaming, flooding, or breakthrough and decide corrective actions
- •Set conservative operating margins to protect availability, equipment integrity, and emissions compliance
Automation
- •Apply fixed control logic and alarm thresholds to maintain basic operating limits
- •Trend historian and analyzer data for operator visibility
- •Generate standard performance calculations and routine reports
Human Does
- •Approve operating targets and optimization priorities across capture rate, energy use, solvent health, and CO2 purity
- •Review and authorize recommended setpoint changes when risk, compliance, or operability limits are affected
- •Handle exceptions such as abnormal solvent behavior, equipment constraints, analyzer issues, or off-spec product events
AI Handles
- •Continuously predict capture efficiency, energy penalty, solvent condition, and risk of drift under changing loads
- •Recommend or execute optimal operating setpoints within approved constraints to maintain target capture at lower energy use
- •Monitor for early signs of fouling, flooding, foaming, degradation, and equipment performance loss and triage alerts
- •Prioritize corrective actions and performance opportunities using historian, lab, maintenance, and emissions data
Operating Intelligence
How it works
AI runs the operating engine in real time.
Humans govern policy and overrides.
Measured outcomes feed the optimization loop.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Sense
Step 2
Optimize
Step 3
Coordinate
Step 4
Govern
Step 5
Execute
Step 6
Measure
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI senses, optimizes, and coordinates in real time. Humans set policy and override when needed. Measurements close the loop.
The Loop
6 steps
Sense
Take in live demand, capacity, and constraint signals.
Optimize
Continuously compute the best next allocation or action.
Coordinate
Push those actions into systems, channels, or teams.
Govern
Humans set policies, objectives, and overrides.
Authority gates · 1
The system must not change operating targets across capture rate, energy use, solvent health, and CO2 purity without approval from designated operations leadership.[S2][S3][S4]
Why this step is human
Policy decisions affect the entire operating envelope and require organizational authority to change.
Execute
Run the approved operating loop continuously.
Measure
Measured outcomes feed back into the optimization loop.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Carbon Capture Process Optimization implementations:
Key Players
Companies actively working on Carbon Capture Process Optimization solutions: