Bernet Thomas, Wehbe Malak, Febra Sara A, Haslam Andrew J, Adjiman Claire S, Jackson George, Galindo Amparo
Department of Chemical Engineering, Sargent Centre for Process Systems Engineering, Institute for Molecular Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
J Chem Eng Data. 2023 Nov 10;69(2):650-678. doi: 10.1021/acs.jced.3c00358. eCollection 2024 Feb 8.
The prediction of the thermodynamic properties of lactones is an important challenge in the flavor, fragrance, and pharmaceutical industries. Here, we develop a predictive model of the phase behavior of binary mixtures of lactones with hydrocarbons, alcohols, ketones, esters, aromatic compounds, water, and carbon dioxide. We extend the group-parameter matrix of the statistical associating fluid theory SAFT-γ Mie group-contribution method by defining a new cyclic ester group, denoted cCOO. The group is composed of two spherical Mie segments and two association electron-donating sites of type e that can interact with association electron-accepting sites of type H in other molecules. The model parameters of the new cCOO group interactions (1 like interaction and 17 unlike interactions) are characterized to represent target experimental data of physical properties of pure fluids (vapor pressure, single-phase density, and vaporization enthalpy) and mixtures (vapor-liquid equilibria, liquid-liquid equilibria, solid-liquid equilibria, density, and excess enthalpy). The robustness of the model is assessed by comparing theoretical predictions with experimental data, mainly for oxolan-2-one, 5-methyloxolan-2-one, and oxepan-2-one (also referred to as γ-butyrolactone, γ-valerolactone, and ε-caprolactone, respectively). The calculations are found to be in very good quantitative agreement with experiments. The proposed model allows for accurate predictions of the thermodynamic properties and highly nonideal phase behavior of the systems of interest, such as azeotrope compositions. It can be used to support the development of novel molecules and manufacturing processes.
内酯热力学性质的预测是香料、香精和制药行业面临的一项重要挑战。在此，我们开发了一种内酯与烃类、醇类、酮类、酯类、芳香族化合物、水和二氧化碳二元混合物相行为的预测模型。我们通过定义一个新的环状酯基团（表示为cCOO）来扩展统计缔合流体理论SAFT-γ Mie基团贡献法的基团参数矩阵。该基团由两个球形Mie链段和两个e型缔合供电子位点组成，它们可以与其他分子中的H型缔合受电子位点相互作用。新的cCOO基团相互作用的模型参数（1种同类相互作用和17种异类相互作用）经表征以代表纯流体（蒸气压、单相密度和汽化焓）和混合物（气液平衡、液液平衡、固液平衡、密度和过量焓）物理性质的目标实验数据。通过将理论预测与实验数据进行比较来评估模型的稳健性，主要针对氧杂环丁烷-2-酮、5-甲基氧杂环丁烷-2-酮和氧杂环庚烷-2-酮（也分别称为γ-丁内酯、γ-戊内酯和ε-己内酯）。计算结果与实验结果在定量上非常吻合。所提出的模型能够准确预测相关体系的热力学性质和高度非理想的相行为，如共沸物组成。它可用于支持新型分子和制造工艺的开发。
