Group Contribution Method 및 Support Vector Regression 기반 모델을 이용한 방향족 화합물 물성치 예측에 관한 연구

• Published in: 한국안전학회지 Vol. 36; no. 1; pp. 1 - 8
• Main Authors: 강하영, Ha Yeong Kang, 오창보, Chang Bo Oh, 원용선, Yong Sun Won, 유준, J. Jay Liu, 이창준, Chang Jun Lee
• Format: Journal Article
• Language: Korean
• Published: 한국안전학회 28.02.2021
• Subjects: functional group; group contribution method; property estimation; support vector regression; 안전공학; support vector regression; property estimation; group contribution method; functional group
• ISSN: 1738-3803; 2383-9953

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.