Client:
Sector:
Technology:
Optimization of chemical process engineering with hybrid AI
Client
The CHAI project brings together industrial and research partners to develop explainable hybrid AI solutions for chemical process optimization. By combining machine learning and expert knowledge, the project aims to accelerate learning for process engineers, improve decision-making, and scale chemical production more efficiently.
The challenge
Optimizing chemical processes traditionally relies on years of hands-on experience. Chemical engineers interpret the processes to fine-tune production, but:
- Need for chemical data expertise – Specialists typically require years of hands-on experience to interpret sensor-observed data and optimize chemical processes
- Finding skilled talent is a bottleneck – With the increasing demand for chemical products, companies struggle to find enough qualified engineers to maintain and expand their operations.
- Traditional ML models fall short – Most are black-box systems that lack transparency and do not integrate chemical domain expertise.
To scale efficiently and ensure safety, CHAI needed an AI-driven solution that could integrate expert knowledge while remaining explainable and adaptive.
The solution
The CHAI project is developing a hybrid AI system that enhances process optimization and decision-making by combining:
- Data-Driven Machine Learning – Identifies patterns from historical data to improve efficiency.
- Knowledge-Driven Models – Incorporates domain expertise to ensure safe and reliable predictions.
- Interactive Dashboards – Deliver real-time, explainable insights to both junior and senior engineers.
- Continuous Feedback Loops – Allows engineers to refine AI predictions based on real-world input.
By making AI transparent and adaptive, process engineers gain better control over complex chemical production workflows without losing critical human expertise.
The key results
- Accelerated Learning – New engineers gain faster insights from AI-assisted decision-making.
- Improved Process Efficiency – Hybrid AI optimizes chemical workflows without compromising safety.
- Enhanced Transparency – Explainable AI ensures trustworthy and actionable recommendations.
- Scalability & Adaptability – The system evolves with engineer feedback to handle changing production conditions.
Scaling Expertise with AI
The CHAI project demonstrates how hybrid AI can bridge knowledge gaps in chemical process engineering, making expertise scalable, transparent, and more efficient.
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