Molecular Modeling of Thermodynamic Properties of Fluids and Fluid Mixtures

流体和流体混合物热力学性质的分子模拟

• 批准号: RGPIN-2018-04212
• 负责人: Smith, William
• 金额: $ 2.04万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712777
• 关键词: Molecular; Modeling; Thermodynamic; Properties; Fluids

The goal of my research program is the development and application of computational algorithms based on fundamental statistical mechanical and quantum mechanical descriptions of the complex fluid systems arising in chemical engineering. The objectives are the prediction of macroscopic thermodynamic properties and elucidation of their underlying molecular basis. Such capabilities are useful in facilitating the improved design of fluid-based systems and processes. Macroscopic models (e.g., equations of state) are conventionally used for property prediction in computer-based process simulators. Although constructed to satisfy the constraints of thermodynamic and other scientific laws, they retain a significant empirical component and contain multiple parameters, whose determination requires significant amounts of experimental data. Our approach is to use relatively simple (requiring relatively few parameters) molecular models describing the underlying system inter- and intra-molecular forces, coupled with their study using the fundamentally exact machinery of statistical mechanics. The rapidly improving capabilities and decreasing costs of computer hardware and software make this approach applicable to increasingly complex systems, and is much less expensive than the use of experimental investigations alone. The goal is to use this methodology to make predictions of the properties and behaviour of previously unstudied compounds and processes, for which experimental data is either unavailable or sparse. Such predictions generate exact pseudo-experimental data for the model systems that can be analyzed and interpreted in the context of conventional macroscopic thermodynamic models. This yields the ability to perform computationally rapid predictions within process simulators. The main impediment to the more widespread application of molecular simulation methodology is the lack of computationally efficient algorithms for the simulation of certain properties and processes. The development of the required algorithms requires the integration of deep understanding of both macroscopic chemical engineering thermodynamics and the statistical mechanics of complex fluid mixtures, in both of which my group has considerable experience. The proposed project focuses on the development and applications of algorithms for the simulation of chemical reaction and phase equilibria, and on the modeling of thermochemical properties of aqueous electrolyte systems. A goal of the former project is to improve upon the primary existing algorithm in that area, developed by my group in the mid 1990's and which is now in widespread use by researchers worldwide. Applications of this proposal encompass a wide variety of processes and applications, including both traditional engineering fluid systems and aqueous systems arising in biology.

我的研究计划的目标是基于化学工程中出现的复杂流体系统的基本统计力学和量子力学描述来开发和应用计算算法。目标是预测宏观热力学性质并阐明其潜在的分子基础。这些功能有助于促进基于流体的系统和过程的改进设计。 宏观模型（例如状态方程）通常用于基于计算机的过程模拟器中的性能预测。尽管它们的构造是为了满足热力学和其他科学定律的约束，但它们保留了重要的经验成分并包含多个参数，其确定需要大量的实验数据。 我们的方法是使用相对简单（需要相对较少的参数）的分子模型来描述底层系统的分子间和分子内力，并结合使用基本精确的统计力学机制进行研究。 计算机硬件和软件的快速提高的能力和不断降低的成本使得这种方法适用于日益复杂的系统，并且比单独使用实验研究便宜得多。 目标是使用这种方法来预测以前未研究的化合物和过程的性质和行为，这些化合物和过程的实验数据要么不可用，要么稀疏。 这种预测为模型系统生成精确的伪实验数据，可以在传统的宏观热力学模型的背景下进行分析和解释。 这使得能够在过程模拟器中进行快速计算预测。 分子模拟方法更广泛应用的主要障碍是缺乏计算有效的算法来模拟某些性质和过程。 所需算法的开发需要结合对宏观化学工程热力学和复杂流体混合物的统计力学的深刻理解，我的团队在这两个方面都拥有丰富的经验。 该项目重点关注化学反应和相平衡模拟算法的开发和应用，以及水电解质系统热化学性质的建模。 前一个项目的目标是改进该领域现有的主要算法，该算法是我的团队在 1990 年代中期开发的，现在已被全世界的研究人员广泛使用。 该提案的应用涵盖了各种各样的过程和应用，包括传统的工程流体系统和生物学中出现的水系统。