Coal power remains a major source of global emissions, but Artificial Intelligence is making carbon capture more efficient, more reliable, and less expensive to operate. From real-time process control to predictive maintenance, AI is helping retrofit and future-ready plants cut millions of tonnes of CO₂ without crippling energy output.
🎯 How AI Can Make This Product or Solution Much Better
🔄 Dynamic Optimization of Capture Systems
AI adjusts solvent flow, regeneration temperature, and absorber pressure in real time, improving capture efficiency while cutting the plant’s parasitic load by up to 15%.
📊 Integration with Plant Load Changes
Coal plant output often shifts to meet grid demand. AI predicts changes in flue gas flow, CO₂ concentration, and temperature, adjusting capture parameters without performance loss.
🛠 Predictive Maintenance for CCS Units
Machine learning detects early signs of membrane wear, sorbent degradation, and heat exchanger fouling, reducing downtime and extending equipment life.
Our specialty focus areas include
Net Zero by Narsi
Insights and interactions on climate action by Narasimhan Santhanam, Director - EAI
View full playlist📏 Emission Forecasting & Compliance
AI forecasts emissions for different operational scenarios, ensuring plants stay within regulatory limits and avoid costly penalties.
🏗 Retrofit Design Simulation
AI-powered digital twins simulate CCS integration into existing plants, minimizing space needs, optimizing heat recovery, and reducing retrofit costs.
🛠️ How AI Overcomes Key Challenges
| Challenge | AI Solution |
|---|---|
| High energy cost of amine regeneration | AI optimizes heat recovery and integrates low-grade steam sources |
| Variable coal quality & flue gas composition | Adaptive controls maintain CO₂ purity despite feedstock changes |
| Space & retrofit constraints | Digital twins design compact, efficient retrofit layouts |
| CCS cost & public perception | AI reduces operating costs, improving project economics and acceptance |
🤖 Main AI Tools and Concepts Used
- Reinforcement learning for CCS process control
- Predictive analytics for performance and maintenance
- Digital twins for plant + CCS integration design
- Process optimization for energy and cost reduction
- AI-enhanced life cycle assessment tools
📊 Case Studies
- Petra Nova (USA) – AI-enhanced CCS control cut energy penalty by 13%, capturing 1.6M tonnes CO₂/year.
- China Energy Investment Corp – AI predictive analytics improved CCS uptime by 8% at a 1 GW plant.
🚀 Relevant Startups & Providers
| Company | Focus |
|---|---|
| Carbon Clean (UK) | Compact, AI-optimized CCS for coal and industrial retrofits |
| Svante (CA) | AI-enhanced solid sorbent CO₂ capture for high-dust environments |
| Aker Carbon Capture (NO) | Modular CCS with AI process optimization |
| LanzaTech (US) | AI-enabled gas fermentation of captured CO₂ into fuels and chemicals |
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