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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| Vydané v: | Engineering (Beijing, China) Ročník 27; číslo 8; s. 23 - 30 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.08.2023
Laboratory for Chemical Technology,Department of Materials,Textiles and Chemical Engineering,Ghent University,Ghent 9052,Belgium Elsevier |
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| ISSN: | 2095-8099 |
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| Abstract | 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. |
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| AbstractList | 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. 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 pro-cesses 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 cre-ation,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. |
| Author | Ureel, Yannick Van Geem, Kevin M. Dobbelaere, Maarten R. Sabbe, Maarten K. Marin, Guy B. Ouyang, Yi De Ras, Kevin |
| AuthorAffiliation | Laboratory for Chemical Technology,Department of Materials,Textiles and Chemical Engineering,Ghent University,Ghent 9052,Belgium |
| AuthorAffiliation_xml | – name: Laboratory for Chemical Technology,Department of Materials,Textiles and Chemical Engineering,Ghent University,Ghent 9052,Belgium |
| Author_xml | – sequence: 1 givenname: Yannick orcidid: 0000-0001-6883-320X surname: Ureel fullname: Ureel, Yannick – sequence: 2 givenname: Maarten R. orcidid: 0000-0002-8977-8569 surname: Dobbelaere fullname: Dobbelaere, Maarten R. – sequence: 3 givenname: Yi orcidid: 0000-0002-1950-4538 surname: Ouyang fullname: Ouyang, Yi – sequence: 4 givenname: Kevin surname: De Ras fullname: De Ras, Kevin – sequence: 5 givenname: Maarten K. orcidid: 0000-0003-4824-2407 surname: Sabbe fullname: Sabbe, Maarten K. – sequence: 6 givenname: Guy B. orcidid: 0000-0002-6733-1213 surname: Marin fullname: Marin, Guy B. – sequence: 7 givenname: Kevin M. surname: Van Geem fullname: Van Geem, Kevin M. email: Kevin.VanGeem@UGent.be |
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| Issue | 8 |
| Keywords | Bayesian optimization Active learning Chemical engineering Design of experiments Active machine learning |
| Language | English |
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