Chemical solvent CO₂ capture, especially amine-based systems is one of the most mature carbon removal technologies, but it comes with high energy costs, solvent losses, and operational complexity. AI is now transforming how these systems are monitored, optimized, and maintained.
🎯 How AI Can Make This Product or Solution Much Better
📈 Real-Time Solvent Performance Optimization
AI monitors amine concentration, degradation rate, and CO₂ loading in real time, dynamically adjusting solvent circulation, regeneration temperature, and column pressure to keep efficiency at peak levels.
🔮 Predictive Solvent Degradation Management
Machine learning forecasts degradation caused by oxygen ingress, SOx/NOx contamination, or high regeneration temperatures allowing operators to take corrective action before losses occur.
⚡ Minimized Energy Penalty
AI integrates heat recovery, steam cycle management, and reboiler load control to reduce regeneration energy needs by 10–20%.
Our specialty focus areas include
Net Zero by Narsi
Insights and interactions on climate action by Narasimhan Santhanam, Director - EAI
View full playlist🧪 Solvent Formulation Discovery
AI models simulate thousands of potential amine blends, ionic liquids, or hybrids, narrowing candidates for faster lab validation and commercialization.
🌐 Multi-Point Capture Coordination
For large plants, AI prioritizes flue gas streams with higher CO₂ concentrations and lower contaminants, maximizing capture per unit of energy.
🛠️ How AI Overcomes Key Challenges
| Challenge | AI Solution |
|---|---|
| High regeneration energy demand | AI optimizes steam use & integrates waste heat recovery |
| Solvent loss & corrosion | Predictive modeling adjusts inhibitor dosing and avoids hot spots |
| Fluctuating flue gas quality | AI adjusts absorption–desorption in milliseconds |
| High solvent replacement costs | AI degradation prediction extends solvent lifespan by up to 30% |
🤖 Main AI Tools and Concepts Used
- Reinforcement learning for process control
- Machine learning for solvent performance forecasting
- Digital twins for plant simulation
- AI-driven materials discovery for solvent R&D
- Predictive analytics for corrosion control
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