Prediction of Critical Temperature and Critical Volume of Mixtures by a New Group Contribution Method

IF 3.8 3区 工程技术 Q2 ENGINEERING, CHEMICAL Industrial & Engineering Chemistry Research Pub Date : 2024-12-17 DOI:10.1021/acs.iecr.4c03939
Bo Tang, Xiaoyu Yao, Xueqiang Dong, Yanxing Zhao, Maoqiong Gong
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Abstract

The vapor–liquid critical properties of mixtures are essential for defining the vapor–liquid phase boundary and play a crucial role in predicting various thermophysical properties of mixtures, which are particularly significant in applications such as supercritical extraction and transcritical cycles. While experimental measurement is the most efficient and direct approach to obtaining critical properties, it is often time-consuming and labor-intensive, necessitating the use of theoretical prediction methods. Nonetheless, existing prediction models tend to be complex and frequently rely on the critical properties of pure substances, which limit their applicability. In this article, a new group contribution method for predicting the critical temperature and critical volume of mixtures is proposed. The new group contribution method is simple in calculation form, simple in group division, has good accuracy, and does not need the critical temperature and critical volume of pure substances when calculating the critical temperature and critical volume of mixtures. This new method includes 24 groups and can be applied to systems consisting of organic compounds made up of C, H, O, F, Cl, Br, and I elements or CO2. The experimental critical temperatures of 272 compounds and 368 groups of binary mixtures (3223 data points), as well as the experimental critical volumes of 224 compounds and 68 groups of binary mixtures (400 data points), were used to determine the group contribution values and model parameters. The average absolute relative deviations (AARDs) for the correlation of compounds are 1.28% for critical temperature and 4.93% for critical volume. For binary mixtures, the AARDs are 1.62% for the critical temperature and 7.33% for the critical volume. Additionally, the predictive capability of the new method for critical temperature and critical volume has been evaluated. The AARDs for critical temperature are 2.48, 1.98, and 0.94% for 25 data points of pure substances, 615 data points of binary mixtures, and 565 data points of ternary mixtures, respectively. For critical volumes, the AARDs are 5.52% for 26 data points of pure substances and 7.49% for 61 data points of binary mixtures.

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混合物的汽液临界特性对于确定汽液相界至关重要,并在预测混合物的各种热物理性质方面发挥着关键作用,这在超临界萃取和跨临界循环等应用中尤为重要。虽然实验测量是获得临界特性最有效、最直接的方法,但往往耗时耗力,因此必须使用理论预测方法。然而,现有的预测模型往往比较复杂,而且经常依赖于纯物质的临界特性,这限制了其适用性。本文提出了一种预测混合物临界温度和临界体积的新群体贡献法。新的组贡献法计算形式简单,组划分简单,精度高,在计算混合物临界温度和临界体积时不需要纯物质的临界温度和临界体积。这种新方法包括 24 个基团,适用于由 C、H、O、F、Cl、Br 和 I 元素或 CO2 组成的有机化合物体系。利用 272 种化合物和 368 组二元混合物的实验临界温度(3223 个数据点),以及 224 种化合物和 68 组二元混合物的实验临界体积(400 个数据点),确定了组贡献值和模型参数。化合物相关性的平均绝对相对偏差(AARDs)为临界温度的 1.28%和临界体积的 4.93%。对于二元混合物,临界温度的平均绝对相对偏差为 1.62%,临界体积的平均绝对相对偏差为 7.33%。此外,还评估了新方法对临界温度和临界体积的预测能力。对于 25 个纯物质数据点、615 个二元混合物数据点和 565 个三元混合物数据点,临界温度的 AARD 分别为 2.48%、1.98% 和 0.94%。在临界体积方面,纯物质的 26 个数据点的 AARD 值为 5.52%,二元混合物的 61 个数据点的 AARD 值为 7.49%。
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来源期刊
Industrial & Engineering Chemistry Research
Industrial & Engineering Chemistry Research 工程技术-工程:化工
CiteScore
7.40
自引率
7.10%
发文量
1467
审稿时长
2.8 months
期刊介绍: ndustrial & Engineering Chemistry, with variations in title and format, has been published since 1909 by the American Chemical Society. Industrial & Engineering Chemistry Research is a weekly publication that reports industrial and academic research in the broad fields of applied chemistry and chemical engineering with special focus on fundamentals, processes, and products.
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