Group Contribution Method for Group Contribution Method for Estimation of Vapor Liquid Equilibria in Polymer Solutions

Oh, Suk-Yung,Bae, Young-Chan The Polymer Society of Korea 2009 Macromolecular Research Vol.17 No.11

This study introduces a specified group-contribution method for predicting the phase equilibria in polymer solutions. The method is based on a modified double lattice model developed previously. The proposed model includes a combinatorial energy contribution that is responsible for the revised Flory-Huggins entropy of mixing, the van der Waals energy contribution from dispersion, a polar force and specific energy contribution. Using the group-interaction parameters obtained from data reduction, the solvent activities for a large variety of mixtures of polymers and solvents over a wide range of temperatures can be predicted with good accuracy. This method is simple but provides improved predictions compared to those of the other group contribution methods.

Group Contribution Method for Group Contribution Method for Estimation of Vapor Liquid Equilibria in Polymer Solutions

오석영,배영찬 한국고분자학회 2009 Macromolecular Research Vol.17 No.11

This study introduces a specified group-contribution method for predicting the phase equilibria in polymer solutions. The method is based on a modified double lattice model developed previously. The proposed model includes a combinatorial energy contribution that is responsible for the revised Flory-Huggins entropy of mixing, the van der Waals energy contribution from dispersion, a polar force and specific energy contribution. Using the group-interaction parameters obtained from data reduction, the solvent activities for a large variety of mixtures of polymers and solvents over a wide range of temperatures can be predicted with good accuracy. This method is simple but provides improved predictions compared to those of the other group contribution methods.

유기물의 인화점 예측을 위한 부분최소자승법과 SVM의 비교

이창준 ( Chang Jun Lee ),고재욱 ( Jae Wook Ko ),이기백 ( Gi Baek Lee ) 한국화학공학회 2010 Korean Chemical Engineering Research(HWAHAK KONGHA Vol.48 No.6

액체의 화재 및 폭발위험을 나타내는 가장 중요한 물성의 하나인 인화점의 실험 데이터는 그 필요에도 불구하고 실제로 데이터를 확보하는 것이 가능하지 않은 경우가 많다. 이 연구에서는 DIPPR 801에서 얻은 893개 유기물의 인화점 실험데이터로부터 인화점을 예측하는 부분최소자승법(PLS) 및 support vector machine(SVM) 모델을 만들고 비교하였다. 분자를 구성하는 각 구성요소들이 분자의 물성에 일정한 기여를 한다는 가정을 이용하여 분자의 물성을 예측하는 방법인 그룹기여법을 이용하여 65개 작용기가 이 예측모델의 독립변수가 되었고 분자량의 로그값이 추가되었다. 두 모델에서 결정해야 할 매개변수는 교차검증에서 계산된 오차를 이용하여 결정되었는데, SVM모델은 그 매개변수가 많아 particle swarm optimization을 이용한 최적화를 이용하였다. 훈련데이터의 선택이 예측성능에 영향을 줄 수 있어 임의로 100개의 데이터 세트를 생성하여 테스트하였다. 전체 데이터에 대해 계산된 평균절대오차는 PLS가 13.86~14.55였고, SVM이 7.44~10.26여서 SVM이 PLS에 비해 매우 우수한 예측성능을 보였다. The flash point is one of the most important physical properties used to determine the potential for fire and explosion hazards of flammable liquids. Despite the needs of the experimental flash point data for the design and construction of chemical plants, there is often a significant gap between the demands for the data and their availability. This study have built and compared two models of partial least squares(PLS) and support vector machine(SVM) to predict the experimental flash points of 893 organic compounds out of DIPPR 801. As the independent variables of the models, 65 functional groups were chosen based on the group contribution method that was oriented from the assumption that each fragment of a molecule contributes a certain amount to the value of its physical property, and the logarithm of molecular weight was added. The prediction errors calculated from cross-validation were employed to determine the optimal parameters of two models. And, an optimization technique should be used to get three parameters of SVM model. This work adopted particle swarm optimization that is one of heuristic optimization methods. As the selection of training data can affect the prediction performance, 100 data sets of randomly selected data were generated and tested. The PLS and SVM results of the average absolute errors for the whole data range from 13.86 K to 14.55 K and 7.44 K to 10.26 K, respectively, indicating that the predictive ability of the SVM is much superior than PLS.

유기물의 자연발화점 예측을 위한 부분최소자승법과 SVM의 비교

이기백(Gibaek Lee) 한국가스학회 2012 한국가스학회지 Vol.16 No.1

화학물질의 화재위험을 나타내는 가장 중요한 물성의 하나인 자연발화점의 실험 데이터는 그 필요에도 불구하고 데이터를 얻는 것이 어려운 경우가 많다. 이 연구에서는 DIPPR 801에서 얻은 503개 유기물의 자연발화점 실험데이터로부터 자연발화점을 예측하는 부분최소자승법(PLS) 및 support vector machine(SVM) 모델을 만들고 비교하였다. 그룹기여법을 이용하여 59개 작용기가 이 예측모델의 독립변수가 되었다. 두 모델에서 결정해야 할 매개변수는 교차검증으로 계산된 오차를 이용하여 결정되었고, SVM모델은 그 매개변수가 많아 particle swarm optimization을 이용한 최적화를 이용하였다. 전체 데이터에 대해 계산된 평균절대오차는 PLS가 58.59K였고, SVM이 29.11K여서 SVM이 PLS에 비해 매우 우수한 예측성능을 보였다. The autoignition temperature is one of the most important physical properties used to determine the flammability characteristics of chemical substances. Despite the needs of the experimental autoignition temperature data for the design of chemical plants, it is not easy to get the data. This study have built and compared partial least squares (PLS) and support vector machine (SVM) models to predict the autoignition temperatures of 503 organic compounds out of DIPPR 801. As the independent variables of the models, 59 functional groups were chosen based on the group contribution method. The prediction errors calculated from cross-validation were employed to determine the optimal parameters of two models. And, particle swarm optimization was used to get three parameters of SVM model. The PLS and SVM results of the average absolute errors for the whole data range from 58.59K and 29.11K, respectively, indicating that the predictive ability of the SVM is much superior than PLS.

A new estimation algorithm of physical properties based on a group contribution and support vector machine

En Sup Yoon,이창준,이기백,Won So 한국화학공학회 2008 Korean Journal of Chemical Engineering Vol.25 No.3

There are two ways to evaluate the properties of unknown chemical compounds. One is by traditional approaches, which measure the desired data from the experiments and the other is by predicting them in the theoretical approaches using a kind of prediction model. The latter are considered to be more effective because they are less time consuming and cost efficient, and there is less risk in conducting the experiments. Besides, it is inconvenient to conduct experiments to obtain experimental data, especially for new materials or high molecular substances. Several methods using regression model and neural network for predicting the physical properties have been suggested so far. However, the existing methods have many problems in terms of accuracy and applicability. Therefore, an improved method for predicting the properties is needed. A new method for predicting the physical property was proposed to predict 15 physical properties for the chemicals which consist of C, H, N, O, S and Halogens. This method was based on the group contribution method that was oriented from the assumption that each fragment of a molecule contributes a certain amount to the value of its physical property. In order to improve the accuracy of the prediction of the physical properties and the applicability, we extended the database, significantly modifying the existing group contribution methods, and then established a new method for predicting the physical properties using support vector machine (SVM) which is a statistical theory that has never been used for predicting the physical properties. The SVM-based approach can develop nonlinear structure property correlations more accurately and easily in comparison with other conventional approaches. The results from the new estimation method are found to be more reliable, accurate and applicable. The newly proposed method can play a crucial role in the estimation of new compounds in terms of the expense and time.

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

강하영,오창보,원용선,유준,이창준,Kang, Ha Yeong,Oh, Chang Bo,Won, Yong Sun,Liu, J. Jay,Lee, Chang Jun 한국안전학회 2021 한국안전학회지 Vol.36 No.1

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.

Consideration of Long and Middle Range Interaction on the Calculation of Activities for Binary Polymer Solutions

Lee, Seung-Seok,Bae, Young-Chan,Sun, Yang-Kook,Kim, Jae-Jun The Polymer Society of Korea 2008 Macromolecular Research Vol.16 No.4

We established a thermodynamic framework of group contribution method based on modified double lattice (MDL) model. The proposed model included the long-range interaction contribution caused by the Coulomb electrostatic forces, the middle-range interaction contribution from the indirect effects of the charge interactions and the short-range interaction from modified double lattice model. The group contribution method explained the combinatorial energy contribution responsible for the revised Flory-Huggins entropy of mixing, the van der Waals energy contribution from dispersion, the polar force, and the specific energy contribution from hydrogen bonding. We showed the solvent activities of various polymer solution systems in comparison with theoretical predictions based on experimental data. The proposed model gave a very good agreement with the experimental data.

Group Contribution Method for Group Contribution Method for Estimation of Vapor Liquid Equilibria in Polymer Solutions

Suk Yung Oh,Young Chan Bae 한국고분자학회 2009 Macromolecular Research Vol.17 No.11

Molecular cohesive energy prediction of bioantioxidants by functional group contribution method

이형진,김형수,정수경,홍인권 한국공업화학회 2009 한국공업화학회 연구논문 초록집 Vol.2009 No.1

Flash point prediction of organic compounds using a group contribution and support vector machine

이창준,Gibaek Lee,고재욱 한국화학공학회 2012 Korean Journal of Chemical Engineering Vol.29 No.2

The flash point is one of the most important properties of flammable liquids. This study proposes a support vector regression (SVR) model to predict the flash points of 792 organic compounds from the DIPPR 801 database. The input variables of the model consist of 65 different functional groups, logarithm of molecular weight and their boiling points in this study. Cross-validation and particle swarm optimization were adopted to find three optimal parameters for the SVR model. Since the prediction largely relies on the selection of training data, 100 training data sets were randomly generated and tested. Moreover, all of the organic compounds used in this model were divided into three major classes, which are non-ring, aliphatic ring, and aromatic ring, and a prediction model was built accordingly for each class. The prediction results from the three-class model were much improved than those obtained from the previous works, with the average absolute error being 5.11-7.15 K for the whole data set. The errors in calculation were comparable with the ones from experimental measurements. Therefore, the proposed model can be implemented to determine the initial flash point for any new organic compounds.