Evolving an Accurate Decision Tree‐Based Model for Predicting Carbon Dioxide Solubility in Polymers,Chemical Engineering & Technology

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Evolving an Accurate Decision Tree‐Based Model for Predicting Carbon Dioxide Solubility in Polymers
Chemical Engineering & Technology ( IF 1.8 ) Pub Date : 2020-01-21 , DOI: 10.1002/ceat.201900096
Reza Soleimani ₁ , Amir Hossein Saeedi Dehaghani ₂ , Ali Rezai-Yazdi ₃ , Seyed Abolhassan Hosseini ₄ , Seyedeh Pegah Hosseini ₁ , Alireza Bahadori ₅

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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.

中文翻译：

进化基于精确决策树的模型来预测聚合物中的二氧化碳溶解度

溶解度是决定共混体系各组分相容性的最不可缺少的物理化学性质之一。作为绿色化学的重要应用，研究集中在二氧化碳在聚合物中的溶解度。为了替代昂贵且费时的实验，基于515条已发布的实验数据线，提出了一种基于决策树的新型溶解度预测模型，称为随机梯度增强算法，用于预测13种不同聚合物中的CO ₂溶解度。结果表明，提出的集成模型是预测CO ₂的有效方法。在各种聚合物中的溶解性，具有非常令人满意的性能和高效率。它比其他方法（例如机器学习方案和状态方程方法）产生更准确的输出。

更新日期：2020-01-21

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