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Appl. Sci. 2016, 6(1), 25; 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

1
College of Chemistry, Sichuan University, Chengdu 610064, China
2
College of Mathematics, Sichuan University, Chengdu 610064, China
3
College of Light Industry, Textile and Food Science Engineering, Sichuan University, Chengdu 610064, China
4
Software School, Xiamen University, Xiamen 361005, China
5
Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China
6
School of Computing, Informatics, Decision Systems Engineering (CIDSE), Ira A. Fulton Schools of Engineering, Arizona State University, Tempe 85281, AZ, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Christian Dawson
Received: 18 December 2015 / Revised: 12 January 2016 / Accepted: 12 January 2016 / Published: 19 January 2016
(This article belongs to the Special Issue Applied Artificial Neural Network)
View Full-Text   |   Download PDF [1641 KB, uploaded 19 January 2016]   |  

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. View Full-Text
Keywords: 1,1,1,2,3,3,3-heptafluoropropane; R227ea; Song and Mason equation; machine learning; support vector machine; artificial neural networks 1,1,1,2,3,3,3-heptafluoropropane; R227ea; Song and Mason equation; machine learning; support vector machine; artificial neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, H.; Tang, X.; Wang, R.; Lin, F.; Liu, Z.; Cheng, K. 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. Appl. Sci. 2016, 6, 25.

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