Journal article
Evolving an Accurate Decision Tree‐Based Model for Predicting Carbon Dioxide Solubility in Polymers
Chemical Engineering & Technology, Vol.43(3), pp.514-522
03/2020
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Source: InCites
Abstract
Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time-consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO2 solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO2 solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach.
Details
- Title
- Evolving an Accurate Decision Tree‐Based Model for Predicting Carbon Dioxide Solubility in Polymers
- Creators
- Reza Soleimani - Tarbiat Modares UniversityAmir Hossein Saeedi Dehaghani - Tarbiat Modares UniversityAli Rezai-Yazdi - Aston UniversitySeyed Abolhassan Hosseini - University of AlbertaSeyedeh Pegah Hosseini - Tarbiat Modares UniversityAlireza Bahadori - Southern Cross University
- Publication Details
- Chemical Engineering & Technology, Vol.43(3), pp.514-522
- Publisher
- Wiley-VCH Verlag GmbH & Co. KGaA
- Number of pages
- 9
- Identifiers
- 991012926984302368
- Copyright
- © 2020 WILEY-VCH Verlag GmbH & Co. KGaA
- Academic Unit
- Faculty of Science and Engineering; School of Environment, Science and Engineering; National Centre for Flood Research
- Language
- English
- Resource Type
- Journal article