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

作者机构：Laboratory for Chemical TechnologyDepartment of MaterialsTextiles and Chemical EngineeringGhent UniversityGhent 9052Belgium 

出 版 物：《Engineering》 (工程（英文）)

年 卷 期：2023年第27卷第8期

页      面：23-30页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081704[工学-应用化学] 081104[工学-模式识别与智能系统] 08[工学] 0817[工学-化学工程与技术] 081701[工学-化学工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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) 

主　　题：Active machine learning Active learning Bayesian optimization Chemical engineering Design of experiments 

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 *** learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating processes spanning all length scales of chemical *** active machine learning algorithms are maturing,their applications are falling *** 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.