Solubility predictions through LSBoost for supercritical carbon dioxide in ionic liquids
Solubility predictions through LSBoost for supercritical carbon dioxide in ionic liquids
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DOI：
10.1039/d0nj03868g
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发表时间：
2020-12
影响因子：
3.3
通讯作者：
Yun Zhang;Xiaojie Xu
中科院分区：
文献类型：
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作者：
Yun Zhang;Xiaojie Xu
超临界二氧化碳在离子液体中的溶解度是两相体系化学过程设计的关键参数。测定摩尔溶解度的实验测量需要专门设计的设备，这可能是资源密集型的。或者，预测模型可以通过经由计算智能方法建模来促进溶解度的估计。在目前的工作中，我们开发的最小二乘boosting模型预测超临界二氧化碳在24种离子液体中的溶解度，通过使用离子液体的临界性质和两相系统参数作为描述符。该模型是高度准确的，稳定的，并有希望作为一个快速，强大的，低成本的溶解度估计工具。
The solubility of supercritical carbon dioxide in ionic liquids (ILs) is a key parameter for designing chemical processes in a biphasic system. Experimental measurements to determine the molar solubility requires specially-designed apparatus, which may be resource-intensive. Alternatively, predictive models can facilitate the estimation of the solubility by modeling via computational intelligence approaches. In the current work, we develop the least-squares boosting model to predict the solubility of supercritical carbon dioxide in 24 ionic liquids by using critical properties of ILs and biphasic system parameters as descriptors. The model is highly accurate, stable, and promising as a fast, robust, and low-cost tool for solubility estimations.