Theoretical Chemistry Methodologies to Generate Thermodynamic Properties for Chemical Process Simulation and Pollutant Behavior Prediction (TSE99-G)

加州理工学院的William Goddard、Mario Blanco和John Seinfeld得到了化学系、多学科活动办公室和化学与运输系统系的支持，开发了用于化学过程模拟和污染物行为预测的热力学性质的理论方法。这项资助是在NSF/EPA环境研究伙伴关系（可持续环境技术）下进行的。本研究将量子力学、分子动力学和统计力学整合到下一代化学过程模拟和设计技术中。需要的热力学数据，如多余的自由能，活度系数，相图，将准确地提供第一性原理分子模拟有机溶剂的水混合物。化工生产的效率很大程度上取决于化工工艺设计阶段所采用的热力学数据的质量。由于在化学过程中存在大量可能的二元和多组分混合物，因此通常无法获得此类数据。原子模拟的最新进展，结合预测混合物热力学性质的统计模型，将导致通过更优化的化学过程设计实现污染预防的新方法。

William Goddard, Mario Blanco, and John Seinfeld of the California Institute of Technology are supported by the Division of Chemistry, the Office of Multidisciplinary Activities, and the Division of Chemical and Transport Systems to develop theoretical methodologies which generate thermodynamic properties for use in chemical process simulation and pollutant behavior prediction. This grant is made under the NSF/EPA Partnership for Environmental Research (Technology for a Sustainable Environment). This research will integrate quantum mechanics, molecular dynamics, and statistical mechanics into the next generation of chemical process simulation and design technology. Needed thermodynamic data, such as excess free energies, activity coefficients, and phase diagrams, will be accurately provided by first-principles molecular simulations of aqueous mixtures of organic solvents.Efficiency in chemical manufacturing is greatly dependent on the quality of the thermodynamic data employed during the chemical process design phase. Due to the enormous number of possible binary and multicomponent mixtures present in a chemical process, such data are often unavailable. Recent advances in atomistic simulations, combined with statistical models for predicting thermodynamic properties of mixtures, will lead to a new approach of achieving pollution prevention through more optimal design of chemical processes.