Global deterministic process optimization problems have recently been solved efficiently in a reduced-space by automatic propagation of McCormick relaxations (Bongartz and Mitsos, J. Global Optim, 2017). However, the previous optimizations have been limited to simplified thermodynamic property models. Herein, we propose a method that learns accurate thermodynamic properties via artificial neural networks (ANNs) and integrates those in deterministic global process optimization. The resulting hybrid process model is solved using the recently developed method for deterministic global optimization problems with ANNs embedded (Schweidtmann and Mitsos, J. Optim. Theory Appl., 2018). The optimal operation of a validated steady state model of an organic Rankine cycle is solved as a case study. It is especially challenging as the thermodynamic properties are given by the implicit Helmholtz equation of state. The results show that modeling of thermodynamic properties via ANNs performs favorable in deterministic optimization. This method can rapidly be extended to include properties from existing thermodynamic libraries, based on models or data.

全局确定性过程优化问题最近已通过自动传播McCormick弛豫在缩小的空间中得到有效解决（Bongartz和Mitsos，J.Global Optim，2017年）。但是，以前的优化方法仅限于简化的热力学性质模型。在这里，我们提出了一种通过人工神经网络（ANN）学习准确的热力学性质并将其整合到确定性全局过程优化中的方法。使用最新开发的方法解决嵌入式ANN的确定性全局优化问题，从而解决了混合过程模型的问题（Schweidtmann和Mitsos，J.Optim.Theory Appl。，2018年）。作为案例研究，解决了有机朗肯循环的经过验证的稳态模型的最佳操作。由于热力学性质由隐式的亥姆霍兹状态方程给出，因此特别具有挑战性。结果表明，通过人工神经网络对热力学性质进行建模在确定性优化方面表现良好。基于模型或数据，该方法可以快速扩展为包括现有热力学库中的属性。