Imperial College London
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ProfessorRuthMisener

Faculty of Engineering, Department of Computing

Professor in Computational Optimisation
 
 
 
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Contact

 

+44 (0)20 7594 8315r.misener Website CV

 
 
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Location

 

379Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Thebelt:2022:10.1016/j.ces.2022.117469,
author = {Thebelt, A and Wiebe, J and Kronqvist, JPF and Tsay, C and Misener, R},
doi = {10.1016/j.ces.2022.117469},
journal = {Chemical Engineering Science},
pages = {1--14},
title = {Maximizing information from chemical engineering data sets: Applications to machine learning},
url = {http://dx.doi.org/10.1016/j.ces.2022.117469},
volume = {252},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - It is well-documented how artificial intelligence can have (and already is having) a big impact on chemical engineering. But classical machine learning approaches may be weak for many chemical engineering applications. This review discusses how challenging data characteristics arise in chemical engineering applications. We identify four characteristics of data arising in chemical engineering applications that make applying classical artificial intelligence approaches difficult: (1) high variance, low volume data, (2) low variance, high volume data, (3) noisy / corrupt / missing data, and (4) restricted data with physics-based limitations. For each of these four data characteristics, we discuss applications where these data characteristics arise and show how current chemical engineering research is extending the fields of data science and machine learning to incorporate these challenges. Finally, we identify several challenges for future research.
AU  - Thebelt,A
AU  - Wiebe,J
AU  - Kronqvist,JPF
AU  - Tsay,C
AU  - Misener,R
DO  - 10.1016/j.ces.2022.117469
EP  - 14
PY  - 2022///
SN  - 0009-2509
SP  - 1
TI  - Maximizing information from chemical engineering data sets: Applications to machine learning
T2  - Chemical Engineering Science
UR  - http://dx.doi.org/10.1016/j.ces.2022.117469
UR  - https://www.sciencedirect.com/science/article/pii/S0009250922000537?via%3Dihub
UR  - http://hdl.handle.net/10044/1/94812
VL  - 252
ER  -
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