The chemical industry runs some of the most thermally intensive and complex processes in the world – from cracking and distillation to drying and synthesis. And with that comes an enormous potential for waste heat recovery – most of which still goes unnoticed.
AI is now reshaping WHR in chemicals – turning reactive, multi-stream plants into intelligent energy-recycling ecosystems.
🎯 How AI Can Make This Product or Solution Much Better
🌡️ Real-Time Heat Mapping Across Process Units
AI algorithms process data from reactors, distillation columns, dryers, and flue gas systems to generate granular thermal profiles.
These high-resolution heat maps help identify where and how to recover the most waste heat, even in complex, batch-based or continuous flows.
🔁 Process Integration & Heat Cascade Optimization
AI designs optimal thermal networks using pinch-point analysis and real-time process data.
Reinforcement learning dynamically manages heat exchangers, flow rates, and storage to adapt to shifts in composition or throughput.
Our specialty focus areas include
Net Zero by Narsi
Insights and interactions on climate action by Narasimhan Santhanam, Director - EAI
View full playlist🔍 Predictive Maintenance of Heat Exchangers & Boilers
Machine learning detects early signs of fouling, scaling, or corrosion by analyzing heat transfer data and pressure drops.
This ensures WHR systems run efficiently and avoids unexpected shutdowns or unsafe operation.
⚙️ Dynamic Matching of Waste Heat to Utility Loads
AI links recovered heat to internal steam demands, chilled water generation, or absorption cooling—maximizing in-plant reuse.
It can also manage export to CHP systems or nearby industrial partners for energy sharing.
🧯 Safety and Emission Compliance
AI enforces safe limits for WHR operations in hazardous environments.
It also monitors CO₂ reductions and helps meet emissions targets under EU ETS, EPA MACT, or PAT programs in India.
🛠️ How AI Overcomes Key Challenges
| Challenge | AI Solution |
|---|---|
| Highly variable batch/continuous processes | AI adapts WHR behavior in real time across shifts in flow and process dynamics |
| Hazardous or corrosive recovery environments | Predictive maintenance and simulation models extend system life and safety |
| Complex utility integration | AI coordinates multiple streams to match heat to real-time demand |
| Missed recovery potential across subsystems | Heat mapping + cascade design enables holistic energy recovery across the plant |
🤖 Main AI Tools and Concepts Used
- Machine learning for heat profile prediction and integration
- Digital twins of reactor, column, and exchanger networks
- Reinforcement learning for WHR cycle and dispatch optimization
- Predictive maintenance for boiler and exchanger reliability
- AI-driven pinch analysis for thermal network design
📊 Case Studies
- BASF (Germany):
Integrated AI-based WHR across steam crackers and reactors, resulting in €60M annual savings and 15–20% recovered energy reuse. - Dow Chemical (USA):
Deployed machine learning to track exchanger performance across its Texas operations, improving heat integration KPIs. - Evonik Industries:
Applied AI simulations and control across fine chemical production sites, increasing WHR performance by 25%.
🚀 Relevant Startups & Providers (TRL 7–9)
| Company | TRL | Highlights |
|---|---|---|
| Arundo Analytics | 8–9 | Advanced analytics and WHR performance tools for chemical processes |
💡 Want More?
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