Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead!
Yannick Ureel,
Maarten R. Dobbelaere,
Yi Ouyang,
Kevin De Ras,
Maarten K. Sabbe,
Guy B. Marin,
Kevin M. Van Geem
Affiliations
Yannick Ureel
Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, Belgium
Maarten R. Dobbelaere
Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, Belgium
Yi Ouyang
Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, Belgium
Kevin De Ras
Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, Belgium
Maarten K. Sabbe
Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, Belgium
Guy B. Marin
Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, Belgium
Kevin M. Van Geem
Corresponding author.; Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, Belgium
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.
