DOI: 10.3390/app6010025. Comparative Study on Theoretical and Machine Learning Methods for Acquiring Compressed Liquid Densities of 1,1,1,2,3,3,3-Heptafluoropropane (R227ea) via Song and Mason Equation, Support Vector Machine, and Artificial Neural Networks

Comparative Study on Theoretical and Machine Learning Methods for Acquiring Compressed Liquid Densities of 1,1,1,2,3,3,3-Heptafluoropropane (R227ea) via Song and Mason Equation, Support Vector Machine, and Artificial Neural Networks

10.3390/app6010025

Crossref journal-article

MDPI AG

Applied Sciences (1968)

Abstract

1,1,1,2,3,3,3-Heptafluoropropane (R227ea) is a good refrigerant that reduces greenhouse effects and ozone depletion. In practical applications, we usually have to know the compressed liquid densities at different temperatures and pressures. However, the measurement requires a series of complex apparatus and operations, wasting too much manpower and resources. To solve these problems, here, Song and Mason equation, support vector machine (SVM), and artificial neural networks (ANNs) were used to develop theoretical and machine learning models, respectively, in order to predict the compressed liquid densities of R227ea with only the inputs of temperatures and pressures. Results show that compared with the Song and Mason equation, appropriate machine learning models trained with precise experimental samples have better predicted results, with lower root mean square errors (RMSEs) (e.g., the RMSE of the SVM trained with data provided by Fedele et al. [1] is 0.11, while the RMSE of the Song and Mason equation is 196.26). Compared to advanced conventional measurements, knowledge-based machine learning models are proved to be more time-saving and user-friendly.

Bibliography

Li, H., Tang, X., Wang, R., Lin, F., Liu, Z., & Cheng, K. (2016). Comparative Study on Theoretical and Machine Learning Methods for Acquiring Compressed Liquid Densities of 1,1,1,2,3,3,3-Heptafluoropropane (R227ea) via Song and Mason Equation, Support Vector Machine, and Artificial Neural Networks. Applied Sciences, 6(1), 25.

Authors 6

Hao Li (first)
Xindong Tang (additional)
Run Wang (additional)
Fan Lin (additional)
Zhijian Liu (additional)
Kewei Cheng (additional)

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Dates

Type	When
Created	9 years, 7 months ago (Jan. 19, 2016, 12:55 p.m.)
Deposited	1 year, 2 months ago (June 4, 2024, 5:59 a.m.)
Indexed	2 months, 3 weeks ago (May 28, 2025, 9:26 a.m.)
Issued	9 years, 7 months ago (Jan. 19, 2016)
Published	9 years, 7 months ago (Jan. 19, 2016)
Published Online	9 years, 7 months ago (Jan. 19, 2016)

Funders 0

None

BibTeX

@article{Li_2016, title={Comparative Study on Theoretical and Machine Learning Methods for Acquiring Compressed Liquid Densities of 1,1,1,2,3,3,3-Heptafluoropropane (R227ea) via Song and Mason Equation, Support Vector Machine, and Artificial Neural Networks}, volume={6}, ISSN={2076-3417}, url={http://dx.doi.org/10.3390/app6010025}, DOI={10.3390/app6010025}, number={1}, journal={Applied Sciences}, publisher={MDPI AG}, author={Li, Hao and Tang, Xindong and Wang, Run and Lin, Fan and Liu, Zhijian and Cheng, Kewei}, year={2016}, month=jan, pages={25} }

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