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

作　　者：Yannick Ureel Maarten R.Dobbelaere Yi Ouyang Kevin De Ras Maarten K.Sabbe Guy B.Marin Kevin M.Van Geem 

机构地区：[1]Laboratory for Chemical Technology,Department of Materials,Textiles and Chemical Engineering,Ghent University,Ghent 9052,Belgium

出　　处：《Engineering》2023年第8期23-30,共8页工程（英文）

基　　金：financial support from the Fund for Scientific Research Flanders(FWO Flanders)through the doctoral fellowship grants(1185822N,1S45522N,and 3F018119);funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(818607)。

摘　　要：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.

关 键 词：Active machine learning Active learning Bayesian optimization Chemical engineering Design of experiments 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TQ02[自动化与计算机技术—控制科学与工程]