Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead

By combining machine learning with the design of experiments, thereby achieving so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating proc...

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Bibliographic Details
Published in:Engineering (Beijing, China) China), 2023-08, Vol.27, p.23-30
Main Authors: Ureel, Yannick, Dobbelaere, Maarten R., Ouyang, Yi, De Ras, Kevin, Sabbe, Maarten K., Marin, Guy B., Van Geem, Kevin M.
Format: Article
Language:English
Subjects:
Active learning
Active machine learning
Bayesian optimization
Chemical engineering
Design of experiments
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Summary:By combining machine learning with the design of experiments, thereby achieving so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating processes spanning all length scales of chemical engineering. While active machine learning algorithms are maturing, their applications are falling behind. In this article, three types of challenges presented by active machine learning—namely, convincing the experimental researcher, the flexibility of data creation, and the robustness of active machine learning algorithms—are identified, and ways to overcome them are discussed. A bright future lies ahead for active machine learning in chemical engineering, thanks to increasing automation and more efficient algorithms that can drive novel discoveries.
ISSN:2095-8099
DOI:10.1016/j.eng.2023.02.019