Deriving activity coefficients from partial excess Raman spectra

从部分过量拉曼光谱导出活度系数

• 批准号: 513859814
• 负责人: Professor Dr.-Ing. Andreas Bräuer
• 金额: --
• 依托单位: Institut für Thermische Verfahrenstechnik, Umwelt- und Naturstoffverfahrenstechnik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/513859814?language=en
• 关键词: Deriving; activity; coefficients; partial; excess

Excess properties of mixtures quantify the difference of the property of a real and an ideal mixture at identical temperature, pressure and composition. According to the “state-of-the-art” the excess Gibbs energy of a mixture can be extracted via partial least squares regression (PLSR) from the excess absorption spectrum of the same mixture. The correlations of excess properties of mixtures feature two disadvantages: (i) they are a function of the mixture compounds. Thus, mixtures of various “families”, such as ketone/alkane or ketone/alcohol, cannot be reflected with one and the same correlation. (ii) The desired activity coefficients can be obtained having fitted gE models to the gE values that resulted from the correlations. These disadvantages can be circumvented, if partial excess properties of the mixture compounds are regarded instead of excess properties of mixtures. Therefore, this project aims at correlating the partial excess Raman spectrum of the mixture compounds with their activity coefficients. The proposed correlation of partial excess properties of mixture compounds features two advantages compared to the “state-of-the-art”: (i) As partial excess properties are regarded, the identified correlations are supposed to be more independent from the mixture family. The degree of independence from the mixture family depends on the correlation method, because of which machine learning methods will be made use of that can regard linear (partial least squares regression) as well as non-linear (convolutional neural networks) relations. (ii) The desired activity coefficients result directly from the identified correlations and do not have to be obtained by fitting first the interaction parameters of gE models. The identified correlations of partial excess properties will enable the experimental Raman spectroscopic determination of activity coefficients of the compounds in a mixture without the need of measuring phase equilibria.

混合物的超特性量化了在相同温度、压力和成分下的真实混合物和理想混合物的特性差异。根据“最先进的技术”，混合物的过量吉布斯能量可以通过偏最小二乘回归（PLSR）从同一混合物的过量吸收光谱中提取出来。混合物过量特性的相关性有两个缺点：(i)它们是混合物化合物的函数。因此，各种“族”的混合物，如酮/烷烃或酮/醇，不能用一个相同的相关性来反映。（ii）通过将gE模型拟合到由相关性产生的gE值，可以获得所需的活度系数。如果考虑混合化合物的部分过量特性而不是混合物的过量特性，则可以避免这些缺点。因此，本项目旨在将混合化合物的部分过量拉曼光谱与其活度系数相关联。与“最先进的”方法相比，所提出的混合化合物部分过量特性的相关性具有两个优点：(i)由于考虑了部分过量特性，所确定的相关性应该更独立于混合物族。与混合族的独立程度取决于相关方法，因此将使用可以考虑线性（偏最小二乘回归）和非线性（卷积神经网络）关系的机器学习方法。（ii）期望的活度系数直接来自已确定的相关性，不必首先通过拟合通用电气模型的相互作用参数来获得。所确定的部分过量性质的相关性将使实验拉曼光谱测定混合物中化合物的活度系数而不需要测量相平衡。