Group Contribution Method and Support Vector Regression based Model for Predicting Physical Properties of Aromatic Compounds 


Vol. 36,  No. 1, pp. 1-8, Feb.  2021
10.14346/JKOSOS.2021.36.1.1


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  Abstract

To simulate a process model in the field of chemical engineering, it is very important to identify the physical properties of novel materials as well as existing materials. However, it is difficult to measure the physical properties throughout a set of experiments due to the potential risk and cost. To address this, this study aims to develop a property prediction model based on the group contribution method for aromatic chemical compounds including benzene rings. The benzene rings of aromatic materials have a significant impact on their physical properties. To establish the prediction model, 42 important functional groups that determine the physical properties are considered, and the total numbers of functional groups on 147 aromatic chemical compounds are counted to prepare a dataset. Support vector regression is employed to prepare a prediction model to handle sparse and high-dimensional data. To verify the efficacy of this study, the results of this study are compared with those of previous studies. Despite the different datasets in the previous studies, the comparison indicated the enhanced performance in this study. Moreover, there are few reports on predicting the physical properties of aromatic compounds. This study can provide an effective method to estimate the physical properties of unknown chemical compounds and contribute toward reducing the experimental efforts for measuring physical properties.

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  Cite this article

[IEEE Style]

강하영, 오창보, 원용선, 유준, 이창준, "Group Contribution Method and Support Vector Regression based Model for Predicting Physical Properties of Aromatic Compounds," Journal of the Korean Society of Safety, vol. 36, no. 1, pp. 1-8, 2021. DOI: 10.14346/JKOSOS.2021.36.1.1.

[ACM Style]

강하영, 오창보, 원용선, 유준, and 이창준. 2021. Group Contribution Method and Support Vector Regression based Model for Predicting Physical Properties of Aromatic Compounds. Journal of the Korean Society of Safety, 36, 1, (2021), 1-8. DOI: 10.14346/JKOSOS.2021.36.1.1.

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