Evolving an Accurate Decision Tree-Based Model for Predicting Carbon Dioxide Solubility in Polymers |
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| Authors: | Reza Soleimani Amir Hossein Saeedi Dehaghani Ali Rezai-Yazdi Seyed Abolhassan Hosseini Seyedeh Pegah Hosseini Alireza Bahadori |
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| Affiliation: | 1. Tarbiat Modares University, Faculty of Chemical Engineering, P.O. Box, 14115-143 Tehran, Iran;2. Tarbiat Modares University, Department of Petroleum Engineering, Faculty of Chemical Engineering, 14115-143 Tehran, Iran;3. Aston University, Engineering & Applied Science School, Birmingham, United Kingdom;4. University of Alberta, Department of Mechanical Engineering, Donadeo Innovation Center for Engineering, T6G 1H9 Edmonton, AB, Canada;5. Southern Cross University, School of Environment, Science and Engineering, 2480 Lismore, New South Wales, Australia |
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| 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. |
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| Keywords: | Carbon dioxide Polymers Solubility prediction Stochastic gradient boosting |
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