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Phase Equilibria Measurements for Predictions by Group Contributions

Phase Equilibria Measurements for Predictions by Group Contributions
通过组贡献进行预测的相平衡测量
批准号:
8713233
负责人:
John Cunningham
金额:
$7.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
1988
资助国家:
美国
项目状态:
已结题
起止时间:
1988-02-15 至 1994-08-31

项目摘要

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中文摘要
翻译
基于相平衡的分离(以及非理想液体混合物的热力学性质)对于设计高效的化学过程通常是必不可少的。除了大量感兴趣的混合物中的一小部分之外,无法进行昂贵和耗时的测量。UNIFAC和ASOG等基团贡献方法在预测非理想液体混合物的相平衡方面显示出很大的前景。然而,这些方法目前受到可用的实验数据来描述群体相互作用的限制。由AIChE下属的物理性质数据设计院(DIPPR)指导的前期工作,在获得二元相互作用参数以填补UNIFAC表空白所需的测量数据方面取得了进展。需要做更多的工作来完善这些表格,并使该方法更普遍地有用和适用。测量了二元液体混合物的气液平衡和溶解度数据,用于改进基团贡献方法。具体系统的选择考虑到其他工作,以避免重复工作。DIPPR在处理项目方面实现了显著的优势,因为DIPPR广泛地代表了化学工程专业和化学过程工业,对项目的结果有着强烈的持续兴趣,有竞争力地选择研究人员,并根据与从事这项工作的研究人员签订的合同管理项目。
英文摘要
Separations based on phase equilibria (and thus thermodynamic properties of nonideal liquid mixtures) are often essential for design of efficient chemical processes. Costly and time consuming measurements cannot be made except for a tiny fraction of the enormous number of mixtures of interest. Group contribution methods such as UNIFAC and ASOG show great promise for predicting phase equilibria of nonideal liquid mixtures. However, these methods are presently limited by the available experimental data to describe the group interactions. Previous work directed by Design Institute for Physical Property Data (DIPPR), under AIChE, has made progress in measuring data needed to obtain binary interaction parameters to fill in the blanks in the UNIFAC tables. Much additional work is needed to complete the tables and make the method more generally useful and applicable. Vapor-liquid equilibrium and solubility data are measured for binary liquid mixtures which is used to enhance the group contribution methods. Specific systems are selected taking other work into account to avoid duplication of effort. Significant advantages are realized in the handling of the project by DIPPR, since DIPPR broadly represents the chemical engineering profession and the chemical process industries, has a strong ongoing interest in the results of the projects, selects researchers competitively, and administers projects under contract with the researchers doing the work.
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  • 批准号:
    EP/V004743/1
  • 项目类别:
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  • 资助金额:
    $127.28万
  • 财政年份:
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  • 项目类别:
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  • 资助金额:
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  • 财政年份:
    2017
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33rd International Conference on Machine Learning (ICML 2016)
  • 批准号:
    1630365
  • 项目类别:
    Standard Grant
  • 资助金额:
    $3.0万
  • 财政年份:
    2016
  • 负责人:
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  • 项目类别:
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  • 资助金额:
    $51.68万
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    2015
  • 负责人:
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  • 依托单位:
海外基金