RoboRana Group, Allnex, P&G, and imec worked together to increase the productivity of chemistry engineers by leveraging expert knowledge and feedback through Hybrid AI.
Chemical production facilities face a critical bottleneck in scaling their operations: a heavy reliance on experienced process engineers. These specialists typically require years of hands-on experience to interpret sensor-observed data and optimize chemical processes. With the increasing demand for chemical products, companies struggle to find enough qualified engineers to maintain and expand their operations. Traditional machine learning solutions provide limited improvements because they cannot incorporate valuable human expertise and often result in "black box" decisions that engineers cannot trust.
The CHAI project introduces a groundbreaking approach: explainable hybrid AI algorithms combining the best of human expertise and machine learning. This innovative system:
"The CHAI consortium will use ML that incorporates expert knowledge for better outcome prediction and control measures. This will enable the scaling of expertise of process operators and engineers, regardless of their experience"
The implementation of CHAI's hybrid AI solution transforms chemical process optimization by:
CHAI's hybrid AI platform stands out through its unique ability to automatically translate complex data streams into actionable insights. The system continuously learns from operator feedback, adapting to new situations while explaining its suggestions clearly. This creates a virtuous cycle where AI and human operators become more effective over time, leading to increasingly optimized chemical processes.
“Creating flexible and resilient organizations driven by data, people and technology."
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