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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-08-01
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| Series: | Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095809923002862 |
| _version_ | 1797261826343305216 |
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| author | Yannick Ureel Maarten R. Dobbelaere Yi Ouyang Kevin De Ras Maarten K. Sabbe Guy B. Marin Kevin M. Van Geem |
| author_facet | Yannick Ureel Maarten R. Dobbelaere Yi Ouyang Kevin De Ras Maarten K. Sabbe Guy B. Marin Kevin M. Van Geem |
| author_sort | Yannick Ureel |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T23:47:23Z |
| format | Article |
| id | doaj.art-29100d55beed4b9782bcc74984d86ce8 |
| institution | Directory Open Access Journal |
| issn | 2095-8099 |
| language | English |
| last_indexed | 2024-04-24T23:47:23Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering |
| spelling | doaj.art-29100d55beed4b9782bcc74984d86ce82024-03-15T04:43:20ZengElsevierEngineering2095-80992023-08-01272330Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead!Yannick Ureel0Maarten R. Dobbelaere1Yi Ouyang2Kevin De Ras3Maarten K. Sabbe4Guy B. Marin5Kevin M. Van Geem6Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, BelgiumLaboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, BelgiumLaboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, BelgiumLaboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, BelgiumLaboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, BelgiumLaboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, BelgiumCorresponding author.; Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent 9052, BelgiumBy 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.http://www.sciencedirect.com/science/article/pii/S2095809923002862Active machine learningActive learningBayesian optimizationChemical engineeringDesign of experiments |
| spellingShingle | Yannick Ureel Maarten R. Dobbelaere Yi Ouyang Kevin De Ras Maarten K. Sabbe Guy B. Marin Kevin M. Van Geem Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead! Engineering Active machine learning Active learning Bayesian optimization Chemical engineering Design of experiments |
| title | Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead! |
| title_full | Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead! |
| title_fullStr | Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead! |
| title_full_unstemmed | Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead! |
| title_short | Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead! |
| title_sort | active machine learning for chemical engineers a bright future lies ahead |
| topic | Active machine learning Active learning Bayesian optimization Chemical engineering Design of experiments |
| url | http://www.sciencedirect.com/science/article/pii/S2095809923002862 |
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