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Comparison of Partial Least Squares and Support Vector Machine for the Flash Point Prediction of Organic Compounds  

Lee, Chang Jun (Department of Chemical and Biological Engineering, Seoul National University)
Ko, Jae Wook (Department of Chemical Engineering, Kwangwoon University)
Lee, Gibaek (Department of Chemical and Biological Engineering, Chungju National University)
Publication Information
Korean Chemical Engineering Research / v.48, no.6, 2010 , pp. 717-724 More about this Journal
Abstract
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.
Keywords
Flash Point; Property Estimation; Group Contribution Methods; Partial Least Squares; Support Vector Machine; Particle Swarm Optimization;
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Times Cited By KSCI : 1  (Citation Analysis)
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