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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Veröffentlicht in:Engineering (Beijing, China) Jg. 27; H. 8; S. 23 - 30
Hauptverfasser: Ureel, Yannick, Dobbelaere, Maarten R., Ouyang, Yi, De Ras, Kevin, Sabbe, Maarten K., Marin, Guy B., Van Geem, Kevin M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.08.2023
Laboratory for Chemical Technology,Department of Materials,Textiles and Chemical Engineering,Ghent University,Ghent 9052,Belgium
Elsevier
Schlagworte:
Active learning
Active machine learning
Bayesian optimization
Chemical engineering
Design of experiments
Bayesian optimization
Active learning
Chemical engineering
Design of experiments
Active machine learning
ISSN:2095-8099
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Zusammenfassung: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