Imperial College London
Alternate

Professor Erich A. Muller

Faculty of Engineering, Department of Chemical Engineering

Professor of Thermodynamics
 
 
 
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Contact

 

+44 (0)20 7594 1569e.muller Website

 
 
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Assistant

 

Mrs Raluca Reynolds +44 (0)20 7594 5557

 
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Location

 

409ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Joss:2019:10.1021/acs.jchemed.8b00692,
author = {Joss, L and Muller, E},
doi = {10.1021/acs.jchemed.8b00692},
journal = {Journal of Chemical Education},
pages = {697--703},
title = {Machine learning for fluid property correlations: Classroom examples with MATLAB},
url = {http://dx.doi.org/10.1021/acs.jchemed.8b00692},
volume = {96},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Recent advances in computer hardware and algorithms are spawning an explosive growth in the use of computer-based systems aimed at analyzing and ultimately correlating large amounts of experimental and synthetic data. As these machine learning tools become more widespread, it is becoming imperative that scientists and researchers become familiar with them, both in terms of understanding the tools and the current limitations of artificial intelligence, and more importantly being able to critically separate the hype from the real potential. This article presents a classroom exercise aimed at first-year science and engineering college students, where a task is set to produce a correlation to predict the normal boiling point of organic compounds from an unabridged data set of >6000 compounds. The exercise, which is fully documented in terms of the problem statement and the solution, guides the students to initially perform a linear correlation of the boiling point data with a plausible relevant variable (the molecular weight) and to further refine it using multivariate linear fitting employing a second descriptor (the acentric factor). Finally, the data are processed through an artificial neural network to eventually provide an engineering-quality correlation. The problem statements, data files for the development of the exercise, and solutions are provided within a MATLAB environment but are general in nature.
AU  - Joss,L
AU  - Muller,E
DO  - 10.1021/acs.jchemed.8b00692
EP  - 703
PY  - 2019///
SN  - 0021-9584
SP  - 697
TI  - Machine learning for fluid property correlations: Classroom examples with MATLAB
T2  - Journal of Chemical Education
UR  - http://dx.doi.org/10.1021/acs.jchemed.8b00692
UR  - http://hdl.handle.net/10044/1/66845
VL  - 96
ER  -
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