Cement production emits over 2.5 billion tonnes of CO₂ each year. Artificial Intelligence is now making carbon capture systems more efficient, less costly, and easier to integrate into cement plants, turning a climate challenge into a circular economy opportunity.
🎯 How AI Can Make This Product or Solution Much Better
⚙️ Optimization of Capture Processes
AI fine-tunes solvent regeneration, absorber conditions, and flue gas flow in amine, calcium looping, and oxy-fuel systems, cutting energy penalties by up to 15% while improving capture rates and CO₂ purity.
🔄 Integration with Plant Operations
Machine learning links kiln data, clinker chemistry, and CCS parameters to dynamically adjust capture rates without slowing production, even with variable fuels and feed.
🛡️ Predictive Maintenance for CCS Equipment
AI detects solvent degradation, membrane fouling, and exchanger inefficiencies before they cause downtime – keeping capture systems online and cost-effective.
Our specialty focus areas include
Net Zero by Narsi
Insights and interactions on climate action by Narasimhan Santhanam, Director - EAI
View full playlist📦 CO₂ Transport and Utilization Optimization
AI coordinates storage and utilization routes – such as mineralization, synthetic fuels, or chemical, based on live market and logistics data.
🧪 Simulation for Scale-Up and Retrofit
AI-powered digital twins simulate CCS retrofits to existing plants, identifying optimal tie-in points and avoiding costly design errors.
🛠️ How AI Overcomes Key Challenges
| Challenge | AI Solution |
|---|---|
| High cost and energy demand of CCS | Optimized heat integration and regeneration stages |
| Flue gas variability | Adaptive real-time control of sorbent flow and reaction parameters |
| Complex integration with existing plants | AI simulations for retrofit design |
| Market uncertainty for captured CO₂ | AI forecasts demand and links to utilization partners |
🤖 Main AI Tools and Concepts Used
- Process digital twins for capture design and operation
- Reinforcement learning for dynamic control
- Predictive maintenance algorithms for CCS hardware
- Multi-objective optimization for capture rate, energy use, and cost
- AI-enabled LCA for net carbon impact assessment
📊 Case Studies
- Norcem Brevik (Norway) – AI-assisted amine CCS targeting 400,000 t/year CO₂ capture.
- Carbon Clean + Cemex (UK/Mexico) – Modular CCS pilot improved capture rate by 8% and cut energy use.
🚀 Relevant Startups & Providers
| Company | Focus |
|---|---|
| Carbon Clean (UK) | Compact AI-optimized CCS for industrial plants |
| CarbonCure (Canada) | AI-controlled CO₂ injection into concrete |
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