Steelmaking accounts for 7–9% of global CO₂ emissions, with blast furnace – basic oxygen furnace (BF–BOF) routes being especially carbon-intensive. AI is transforming how carbon capture systems are designed, integrated, and operated in steel plants – cutting costs and boosting capture efficiency.
🎯 How AI Can Make This Product or Solution Much Better
🔄 Process-Integrated Capture Optimization
AI optimizes integration of post-combustion (amine), pre-combustion, or oxy-fuel capture systems without disrupting steel output, balancing capture rate and production efficiency.
📊 Gas Stream Characterization & Control
Machine learning analyzes off-gas composition, flow, and impurities in real time, adjusting process parameters for maximum CO₂ purity and capture efficiency.
♻ Hybrid Capture Strategy Deployment
AI coordinates partial capture from blast furnace top gas, BOF gas, and reheating furnaces to maximize total CO₂ captured while managing cost and energy use.
Our specialty focus areas include
Net Zero by Narsi
Insights and interactions on climate action by Narasimhan Santhanam, Director - EAI
View full playlist⚡ Energy Optimization
AI links CCS operations with waste heat recovery and combined heat & power systems to minimize fuel demand for solvent regeneration and CO₂ compression.
🔗 Carbon Utilization Pathways
AI dynamically routes captured CO₂ to fuels, chemicals, or mineralization based on market prices, storage availability, and sustainability goals.
🛠️ How AI Overcomes Key Challenges
| Challenge | AI Solution |
|---|---|
| High impurity levels in flue gas | AI-controlled pre-treatment reduces fouling and downtime |
| Space constraints in existing plants | AI-driven 3D layout optimization finds compact retrofit designs |
| High energy cost of capture | AI integrates CCS with WHRUs and optimizes process control to cut penalties |
| Multiple fluctuating emission sources | AI harmonizes capture across varied streams for stable performance |
🤖 Main AI Tools and Concepts Used
- Digital twins of steel plants with CCS modules
- Reinforcement learning for adaptive process control
- ML-based flue gas composition prediction
- AI-optimized waste heat integration
- AI-enabled carbon accounting and LCA software
📊 Case Studies
- ArcelorMittal – Ghent, Belgium – AI-supported Top Gas Recycling + CCS pilot capturing 125,000 tonnes CO₂/year for synthetic fuels.
- Tata Steel (Netherlands) – Machine learning integrated CCS with hydrogen-transition strategy.
- SSAB (Sweden) – Digital twin modeled CCS for both BF–BOF and DRI–EAF steelmaking.
🚀 Relevant Startups & Providers
| Company | Focus |
|---|---|
| Carbon Clean (UK) | Compact, AI-optimized CCS for dusty industrial sites |
| Svante (Canada) | Solid sorbent capture with AI optimization for steel gases |
| LanzaTech (USA) | Gas fermentation of CO₂ into fuels and chemicals |
| Aker Carbon Capture (NO) | Modular CCS adaptable to multiple steel production lines |
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