The availability of property data is one of the major bottlenecks in the development of chemical processes, often requiring time-consuming and expensive experiments or limiting the design space to a small number of known molecules. This bottleneck has been the motivation behind the continuing development of predictive property models. For the property prediction of novel molecules, group contribution methods have been groundbreaking. In recent times, machine learning has joined the more established property prediction models. However, even with recent successes, the integration of physical constraints into machine learning models remains challenging. Physical constraints are vital to many thermodynamic properties, such as the Gibbs-Dunham relation, introducing an additional layer of complexity into the prediction. Here, we introduce SPT-NRTL, a machine learning model to predict thermodynamically consistent activity coefficients and provide NRTL parameters for easy use in process simulations. The results show that SPT-NRTL achieves higher accuracy than UNIFAC in the prediction of activity coefficients across all functional groups and is able to predict many vapor-liquid-equilibria with near experimental accuracy, as illustrated for the exemplary mixtures water/ethanol and chloroform/n-hexane. To ease the application of SPT-NRTL, NRTL-parameters of 100 000 000 mixtures are calculated with SPT-NRTL and provided online.
翻译:财产数据的提供是化学过程发展的主要瓶颈之一,往往需要花费时间和昂贵的实验,或将设计空间限制在少数已知的分子中。这种瓶颈是继续开发预测性财产模型背后的动力。对于新分子的财产预测来说,群体贡献方法具有突破性。近些年来,机器学习加入了较为成熟的财产预测模型。但即使最近取得了成功,将物理限制纳入机器学习模型仍然具有挑战性。物理限制对许多热动力特性至关重要,例如Gibbbs-Dunham关系,给预测增加了一层复杂性。在这里,我们引入了TV-NRTL,这是一个机器学习模型,用来预测热力一致的活动系数,并提供NRTL参数,便于在过程模拟中使用。结果表明,在预测所有功能组的活动系数方面,TV-NRTL比UNIFAC更加精确,并且能够预测许多液体-液体-平衡性能特性,而且几乎具有试验性精度,如用于示范性混合物/乙醇和NMYMR-MER-000-ML的计算,提供方便应用。