期刊
JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS
卷 46, 期 -, 页码 205-213出版社
ELSEVIER
DOI: 10.1016/j.jtice.2014.09.015
关键词
Solubility; SVM model; Polystyrene, poly vinyl acetate, polypropylene, poly butylene succinate-co-adipate; Poly butylene succinate
This paper concerns with implementation of support vector machine algorithm for developing improved models capable of predicting the solubility of CO2 in five different polymers namely polystyrene (PS), poly vinyl acetate (PVAC), polypropylene (PP), poly butylene succinate-co-adipate (PBSA) and poly butylene succinate (PBS). Validity of the presented models has been evaluated by utilizing several statistical parameters. The predictions of the developed models for polymers of PS, PVAC, PP, PBSA, PBS are in excellent agreement with corresponding experimental data with the average absolute relative deviation percent (%AARD) equal to %0.151, %0.500, %1.381, %0.158, %0.239 and R-2 values of greater than 0.999. Furthermore, the estimation capability of the proposed models has been compared to a wellknown equation of state (EOS) as well as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. According to the results of comparative studies, it was found that the developed models are more robust, reliable and efficient than other existing techniques for improved analysis and design of polymer processing technology. (C) 2014 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
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